Mixed Effect Models
Megan
2025-03-13
Mixed effects models - random effects by participant ID
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## i Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(ggplot2)
library(broom.mixed)
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## steplibrary(performance)To - do and Questions
Home videos:
- anything else to account for R and left turn vidoes? Try running on just right and just left turn data?
- Other test more appropriate for home video? outcomes not normally distributed and QQ plots look bad…..
Home videos
- Mat FW as outcome and video metrics as predictor?
Improve metric - re-run delta pixel eye to foot?
Dropped rows with any missing video metrics for adj vs unadj metrics
- Should this be done before univariate steps?
Interaction? - test diff with aov()
Set directories and load data
output directory
# analysis folder
analysis_version <- '005'output_dir <- file.path("C:/Users/mmccu/Box/MM_Personal/5_Projects/BoveLab/3_Data_and_Code/gait_bw_zeno_home_analysis",
analysis_version,
"003_mixed_effects_video_metric_vs_outcomes")
# create output folder if it doesn't already exist
if (file.exists(output_dir) == FALSE){
dir.create(output_dir)
}Load and format PWS data
All videos included in analysis.
Each row = 1 video
# Preferred Walking Speed
zeno_pws_path <- file.path("C:/Users/mmccu/Box/MM_Personal/5_Projects/BoveLab/3_Data_and_Code/gait_bw_zeno_home_analysis",
analysis_version,
"000_merged_cleaned_data",
"zv_bw_merged_gait_vertical_PWS_1_clean.csv")
zeno_pws_df <- read.csv(zeno_pws_path)
table(zeno_pws_df$task_pose)##
## gait_vertical_PWS_1
## 224Factor categorical vars
str(zeno_pws_df)## 'data.frame': 224 obs. of 85 variables:
## $ X : int 0 1 3 4 5 6 7 8 10 11 ...
## $ video_id_date_name_pose_zv : chr "gait_vertical_PWS_1_BW-0002_2022_09_12" "gait_vertical_PWS_1_BW-0002_2023_09_12" "gait_vertical_PWS_1_BW-0004_2022_09_19" "gait_vertical_PWS_1_BW-0006_2022_09_26" ...
## $ id_date_pose_zv : chr "BW-0002\\2022_09_12" "BW-0002\\2023_09_12" "BW-0004\\2022_09_19" "BW-0006\\2022_09_26" ...
## $ task_pose_zv : chr "gait_vertical_PWS_1" "gait_vertical_PWS_1" "gait_vertical_PWS_1" "gait_vertical_PWS_1" ...
## $ frames_per_second_pose_zv : int 25 30 30 30 30 30 30 30 30 30 ...
## $ total_video_duration_sec_pose_zv : num 42.8 46.6 30.4 24.4 27.3 ...
## $ delta_pix_h_rel_median_pose_zv : num 0.15 0.22 0.37 0.36 0.26 NA 0.34 NA 0.28 0.27 ...
## $ walking_segmets_n_pose_zv : num NA 2 NA 2 4 2 2 4 3 3 ...
## $ walking_segments_duration_mean_pose_zv : num NA 5.62 NA 4.65 3.95 4.48 4.55 6.94 3.6 4.79 ...
## $ walking_segments_duration_median_pose_zv : num NA 5.62 NA 4.65 4.05 4.48 4.55 6.88 3.93 5 ...
## $ stride_time_mean_sec_pose_zv : num NA 1.47 NA 1.15 1.17 ...
## $ stride_time_median_sec_pose_zv : num NA 1.5 NA 1.17 1.17 ...
## $ stride_time_std_pose_zv : num NA 0.254 NA 0.134 0.116 0.09 0.048 0.081 0.166 0.143 ...
## $ stride_time_cv_pose_zv : num NA 17.26 NA 11.71 9.84 ...
## $ stride_time_max_pose_zv : num NA 1.87 NA 1.37 1.47 ...
## $ stride_time_min_pose_zv : num NA 1.033 NA 0.967 0.933 ...
## $ mean_cadence_step_per_min_pose_zv : num NA 91.5 NA 112 92.3 ...
## $ stride_width_mean_cm_pose_zv : num NA 12.5 NA 9.2 10 ...
## $ stride_width_median_cm_pose_zv : num NA 13.5 NA 11.2 11.7 ...
## $ stride_width_std_pose_zv : num NA 4.21 NA 5.52 4.43 ...
## $ stride_width_cv_pose_zv : num NA 33.8 NA 60 44.2 ...
## $ stride_width_max_pose_zv : num NA 16.9 NA 18.8 16.2 ...
## $ stride_width_min_pose_zv : num NA 0.932 NA 0.63 1.131 ...
## $ foot1_gait_cycle_time_mean_pose_zv : num NA 1.23 NA NA 1.23 ...
## $ foot1_stance_time_mean_pose_zv : num NA 1.033 NA NA 0.833 ...
## $ foot1_stance_per_mean_pose_zv : num NA 83.8 NA NA 67.6 ...
## $ foot1_swing_time_mean_pose_zv : num NA 0.2 NA NA 0.4 0.333 NA 0.367 0.383 NA ...
## $ foot1_swing_per_mean_pose_zv : num NA 16.2 NA NA 32.4 ...
## $ foot1_ini_double_support_time_mean_pose_zv : num NA 0.367 NA NA 0.233 0.233 NA 0.233 0.117 NA ...
## $ foot1_term_double_support_time_mean_pose_zv: num NA 0.2 NA NA 0.5 0.2 NA 0.133 0.15 NA ...
## $ foot1_tot_double_support_time_mean_pose_zv : num NA 0.567 NA NA 0.733 0.433 NA 0.367 0.267 NA ...
## $ foot1_double_support_per_mean_pose_zv : num NA 45.9 NA NA 59.5 ...
## $ foot1_single_support_time_mean_pose_zv : num NA 0.467 NA NA 0.1 0.333 NA 0.367 0.433 NA ...
## $ foot1_single_support_per_mean_pose_zv : num NA 37.84 NA NA 8.11 ...
## $ walk_segment_pose_zv : num NA 1 NA NA 2 1 NA 0 0 NA ...
## $ foot1_pose_zv : chr "" "right" "" "" ...
## $ id_video : chr "BW-0002" "BW-0002" "BW-0004" "BW-0006" ...
## $ visit_date_video : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ bw_id : chr "BW-0002" "BW-0002" "BW-0004" "BW-0006" ...
## $ trialdate : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ visit_date : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ demoGait_dateDiff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DiseaseDuration : num 0 1 17 17 7 8 25 26 30 31 ...
## $ clean_sex : chr "Male" "Male" "Female" "Female" ...
## $ clean_race : chr "Other" "Other" "White" "White" ...
## $ clean_ethnicity : chr "Hispanic Or Latino" "Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" ...
## $ demoEHR_Age : num 37 37 46 65 53 53 60 60 71 71 ...
## $ bingoEHR_DX_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "PPMS (Primary-progressive Multiple Sclerosis)" ...
## $ bingoEHR_EDSS_measure_value : num 3.5 3 3 3 3 2 3 3 3.5 3.5 ...
## $ msfcEHR_T25FW.SPEED.AVG : num 5 6.1 4.3 4.6 4.6 4.9 3.7 4.4 5.2 6.1 ...
## $ FW_cadencestepsminmean : num 113 103 134 120 124 ...
## $ FW_singlesupportmean : num 36.8 34.7 39.8 36.5 38.1 ...
## $ FW_singlesupportratiolr : num 1.09 1.11 1.02 1.07 1.01 ...
## $ FW_stridetimeseccv : num 4.01 5.92 3.1 3.12 3.77 ...
## $ FW_stridetimesecmean : num 1.061 1.158 0.891 0.99 0.959 ...
## $ FW_stridewidthcmmean : num 14.76 13.91 7.1 7.55 11.94 ...
## $ FW_stridewidthcmsd : num 4.97 3.75 4.08 3.19 3.08 ...
## $ FW_totaldsupportmean : num 27.3 30.5 20.4 26.9 24 ...
## $ FW_totaldsupportratiolr : num 0.985 1.003 0.992 0.984 1.046 ...
## $ FW_velocitycmsecmean : num 145 119 197 133 164 ...
## $ PWS_cadencestepsminmean : num 79.4 74.6 100.2 107.3 90.2 ...
## $ PWS_singlesupportmean : num 30.5 30.8 37.1 34.8 32.6 ...
## $ PWS_singlesupportratiolr : num 1.2 1.07 1.14 1.1 1.11 ...
## $ PWS_stridetimeseccv : num 24.5 21.4 23.5 15.5 36.1 ...
## $ PWS_stridetimesecmean : num 1.51 1.61 1.19 1.12 1.33 ...
## $ PWS_stridewidthcmmean : num 16.63 12.11 2.31 8.48 11.43 ...
## $ PWS_stridewidthcmsd : num 11.47 5.32 7.82 16.29 13.71 ...
## $ PWS_totaldsupportmean : num 35.4 35.2 36.4 30.4 30.1 ...
## $ PWS_totaldsupportratiolr : num 1.091 0.912 1.216 0.995 1.077 ...
## $ PWS_velocitycmsecmean : num 66.1 74.1 101.2 94.7 78 ...
## $ redcap_event_name : chr "Brainwalk: Baseline visit (Arm 1: Baseline visit)" "Year 2 Visit (Arm 1: Baseline visit)" "Brainwalk: Baseline visit (Arm 1: Baseline visit)" "Brainwalk: Baseline visit (Arm 1: Baseline visit)" ...
## $ demographic_diagnosis : chr "MS" "MS" "MS" "MS" ...
## $ msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff : num 0 0 0 NA 0 0 0 0 0 0 ...
## $ demoEHR_DX_dateDiff : num 97 462 6289 6296 2645 ...
## $ demoEHR_EDSS_dateDiff : num 0 0 0 0 0 440 0 0 0 -175 ...
## $ bingoEHR_Vitals_height.in. : num 66 NA 67.5 67 NA 68 65 66 63 63 ...
## $ demoEHR_Vitals_dateDiff : num -0.454 -11.391 -0.453 -0.429 -0.552 ...
## $ bingoEHR_first_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "PPMS (Primary-progressive Multiple Sclerosis)" ...
## $ edss_severity_num : num 2 2 2 2 2 1 2 2 2 2 ...
## $ edss_severity_cat : chr "moderate" "moderate" "moderate" "moderate" ...
## $ t25fw_group_num : num 1 2 1 1 1 1 1 1 1 2 ...
## $ t25fw_group_cat : chr "under_6" "6_to_8" "under_6" "under_6" ...
## $ race_ethnicity_clean : chr "Hispanic or Latino" "Hispanic or Latino" "White Not Hispanic" "White Not Hispanic" ...
## $ ms_dx_condensed : chr "RRMS" "RRMS" "RRMS" "Progressive MS" ...zeno_pws_df <- zeno_pws_df %>%
mutate_at(c("video_id_date_name_pose_zv", "id_date_pose_zv", "task_pose_zv",
"id_video", "bw_id", "clean_sex", "bingoEHR_DX_MS.DX",
"demographic_diagnosis", "race_ethnicity_clean",
"race_ethnicity_clean", "ms_dx_condensed",
"redcap_event_name", "bingoEHR_EDSS_measure_value"),
as.factor)
str(zeno_pws_df)## 'data.frame': 224 obs. of 85 variables:
## $ X : int 0 1 3 4 5 6 7 8 10 11 ...
## $ video_id_date_name_pose_zv : Factor w/ 224 levels "gait_vertical_PWS_1_BW-0002_2022_09_12",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ id_date_pose_zv : Factor w/ 224 levels "BW-0002\\2022_09_12",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ task_pose_zv : Factor w/ 1 level "gait_vertical_PWS_1": 1 1 1 1 1 1 1 1 1 1 ...
## $ frames_per_second_pose_zv : int 25 30 30 30 30 30 30 30 30 30 ...
## $ total_video_duration_sec_pose_zv : num 42.8 46.6 30.4 24.4 27.3 ...
## $ delta_pix_h_rel_median_pose_zv : num 0.15 0.22 0.37 0.36 0.26 NA 0.34 NA 0.28 0.27 ...
## $ walking_segmets_n_pose_zv : num NA 2 NA 2 4 2 2 4 3 3 ...
## $ walking_segments_duration_mean_pose_zv : num NA 5.62 NA 4.65 3.95 4.48 4.55 6.94 3.6 4.79 ...
## $ walking_segments_duration_median_pose_zv : num NA 5.62 NA 4.65 4.05 4.48 4.55 6.88 3.93 5 ...
## $ stride_time_mean_sec_pose_zv : num NA 1.47 NA 1.15 1.17 ...
## $ stride_time_median_sec_pose_zv : num NA 1.5 NA 1.17 1.17 ...
## $ stride_time_std_pose_zv : num NA 0.254 NA 0.134 0.116 0.09 0.048 0.081 0.166 0.143 ...
## $ stride_time_cv_pose_zv : num NA 17.26 NA 11.71 9.84 ...
## $ stride_time_max_pose_zv : num NA 1.87 NA 1.37 1.47 ...
## $ stride_time_min_pose_zv : num NA 1.033 NA 0.967 0.933 ...
## $ mean_cadence_step_per_min_pose_zv : num NA 91.5 NA 112 92.3 ...
## $ stride_width_mean_cm_pose_zv : num NA 12.5 NA 9.2 10 ...
## $ stride_width_median_cm_pose_zv : num NA 13.5 NA 11.2 11.7 ...
## $ stride_width_std_pose_zv : num NA 4.21 NA 5.52 4.43 ...
## $ stride_width_cv_pose_zv : num NA 33.8 NA 60 44.2 ...
## $ stride_width_max_pose_zv : num NA 16.9 NA 18.8 16.2 ...
## $ stride_width_min_pose_zv : num NA 0.932 NA 0.63 1.131 ...
## $ foot1_gait_cycle_time_mean_pose_zv : num NA 1.23 NA NA 1.23 ...
## $ foot1_stance_time_mean_pose_zv : num NA 1.033 NA NA 0.833 ...
## $ foot1_stance_per_mean_pose_zv : num NA 83.8 NA NA 67.6 ...
## $ foot1_swing_time_mean_pose_zv : num NA 0.2 NA NA 0.4 0.333 NA 0.367 0.383 NA ...
## $ foot1_swing_per_mean_pose_zv : num NA 16.2 NA NA 32.4 ...
## $ foot1_ini_double_support_time_mean_pose_zv : num NA 0.367 NA NA 0.233 0.233 NA 0.233 0.117 NA ...
## $ foot1_term_double_support_time_mean_pose_zv: num NA 0.2 NA NA 0.5 0.2 NA 0.133 0.15 NA ...
## $ foot1_tot_double_support_time_mean_pose_zv : num NA 0.567 NA NA 0.733 0.433 NA 0.367 0.267 NA ...
## $ foot1_double_support_per_mean_pose_zv : num NA 45.9 NA NA 59.5 ...
## $ foot1_single_support_time_mean_pose_zv : num NA 0.467 NA NA 0.1 0.333 NA 0.367 0.433 NA ...
## $ foot1_single_support_per_mean_pose_zv : num NA 37.84 NA NA 8.11 ...
## $ walk_segment_pose_zv : num NA 1 NA NA 2 1 NA 0 0 NA ...
## $ foot1_pose_zv : chr "" "right" "" "" ...
## $ id_video : Factor w/ 154 levels "BW-0002","BW-0004",..: 1 1 2 3 4 4 5 5 6 6 ...
## $ visit_date_video : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ bw_id : Factor w/ 154 levels "BW-0002","BW-0004",..: 1 1 2 3 4 4 5 5 6 6 ...
## $ trialdate : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ visit_date : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ demoGait_dateDiff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DiseaseDuration : num 0 1 17 17 7 8 25 26 30 31 ...
## $ clean_sex : Factor w/ 3 levels "Female","Male",..: 2 2 1 1 1 1 2 2 1 1 ...
## $ clean_race : chr "Other" "Other" "White" "White" ...
## $ clean_ethnicity : chr "Hispanic Or Latino" "Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" ...
## $ demoEHR_Age : num 37 37 46 65 53 53 60 60 71 71 ...
## $ bingoEHR_DX_MS.DX : Factor w/ 5 levels "MS, Subtype Not Specified",..: 4 4 4 2 4 4 5 5 4 4 ...
## $ bingoEHR_EDSS_measure_value : Factor w/ 13 levels "0","1","1.5",..: 7 6 6 6 6 4 6 6 7 7 ...
## $ msfcEHR_T25FW.SPEED.AVG : num 5 6.1 4.3 4.6 4.6 4.9 3.7 4.4 5.2 6.1 ...
## $ FW_cadencestepsminmean : num 113 103 134 120 124 ...
## $ FW_singlesupportmean : num 36.8 34.7 39.8 36.5 38.1 ...
## $ FW_singlesupportratiolr : num 1.09 1.11 1.02 1.07 1.01 ...
## $ FW_stridetimeseccv : num 4.01 5.92 3.1 3.12 3.77 ...
## $ FW_stridetimesecmean : num 1.061 1.158 0.891 0.99 0.959 ...
## $ FW_stridewidthcmmean : num 14.76 13.91 7.1 7.55 11.94 ...
## $ FW_stridewidthcmsd : num 4.97 3.75 4.08 3.19 3.08 ...
## $ FW_totaldsupportmean : num 27.3 30.5 20.4 26.9 24 ...
## $ FW_totaldsupportratiolr : num 0.985 1.003 0.992 0.984 1.046 ...
## $ FW_velocitycmsecmean : num 145 119 197 133 164 ...
## $ PWS_cadencestepsminmean : num 79.4 74.6 100.2 107.3 90.2 ...
## $ PWS_singlesupportmean : num 30.5 30.8 37.1 34.8 32.6 ...
## $ PWS_singlesupportratiolr : num 1.2 1.07 1.14 1.1 1.11 ...
## $ PWS_stridetimeseccv : num 24.5 21.4 23.5 15.5 36.1 ...
## $ PWS_stridetimesecmean : num 1.51 1.61 1.19 1.12 1.33 ...
## $ PWS_stridewidthcmmean : num 16.63 12.11 2.31 8.48 11.43 ...
## $ PWS_stridewidthcmsd : num 11.47 5.32 7.82 16.29 13.71 ...
## $ PWS_totaldsupportmean : num 35.4 35.2 36.4 30.4 30.1 ...
## $ PWS_totaldsupportratiolr : num 1.091 0.912 1.216 0.995 1.077 ...
## $ PWS_velocitycmsecmean : num 66.1 74.1 101.2 94.7 78 ...
## $ redcap_event_name : Factor w/ 4 levels "Brainwalk: Baseline visit (Arm 1: Baseline visit)",..: 1 2 1 1 1 2 2 3 1 2 ...
## $ demographic_diagnosis : Factor w/ 1 level "MS": 1 1 1 1 1 1 1 1 1 1 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff : num 0 0 0 NA 0 0 0 0 0 0 ...
## $ demoEHR_DX_dateDiff : num 97 462 6289 6296 2645 ...
## $ demoEHR_EDSS_dateDiff : num 0 0 0 0 0 440 0 0 0 -175 ...
## $ bingoEHR_Vitals_height.in. : num 66 NA 67.5 67 NA 68 65 66 63 63 ...
## $ demoEHR_Vitals_dateDiff : num -0.454 -11.391 -0.453 -0.429 -0.552 ...
## $ bingoEHR_first_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "PPMS (Primary-progressive Multiple Sclerosis)" ...
## $ edss_severity_num : num 2 2 2 2 2 1 2 2 2 2 ...
## $ edss_severity_cat : chr "moderate" "moderate" "moderate" "moderate" ...
## $ t25fw_group_num : num 1 2 1 1 1 1 1 1 1 2 ...
## $ t25fw_group_cat : chr "under_6" "6_to_8" "under_6" "under_6" ...
## $ race_ethnicity_clean : Factor w/ 5 levels "Asian","Black Or African American",..: 3 3 5 5 5 5 3 3 5 5 ...
## $ ms_dx_condensed : Factor w/ 3 levels "MS, Subtype Not Specified",..: 3 3 3 2 3 3 2 2 3 3 ...# assign levels to categorical variables
table(zeno_pws_df$race_ethnicity_clean)##
## Asian Black Or African American Hispanic or Latino
## 17 14 21
## Other/Unknown/Declined White Not Hispanic
## 18 154zeno_pws_df$race_ethnicity_clean <- factor(zeno_pws_df$race_ethnicity_clean,
levels = c('White Not Hispanic',
'Asian',
'Black Or African American',
'Hispanic or Latino',
'Other/Unknown/Declined'),
ordered = FALSE)
print(levels(zeno_pws_df$race_ethnicity_clean))## [1] "White Not Hispanic" "Asian"
## [3] "Black Or African American" "Hispanic or Latino"
## [5] "Other/Unknown/Declined"table(zeno_pws_df$ms_dx_condensed)##
## MS, Subtype Not Specified Progressive MS RRMS
## 3 40 181zeno_pws_df$ms_dx_condensed <- factor(zeno_pws_df$ms_dx_condensed,
levels = c('RRMS',
'Progressive MS',
'MS, Subtype Not Specified'),
ordered = FALSE)
print(levels(zeno_pws_df$ms_dx_condensed))## [1] "RRMS" "Progressive MS"
## [3] "MS, Subtype Not Specified"table(zeno_pws_df$clean_sex)##
## Female Male Non-Binary
## 168 54 2zeno_pws_df$clean_sex <- factor(zeno_pws_df$clean_sex,
levels = c('Female',
'Male',
'Non-Binary'),
ordered = FALSE)
print(levels(zeno_pws_df$clean_sex))## [1] "Female" "Male" "Non-Binary"table(zeno_pws_df$edss_severity_cat)##
## mild moderate severe
## 99 81 44zeno_pws_df$edss_severity_cat <- factor(zeno_pws_df$edss_severity_cat,
levels = c('mild',
'moderate',
'severe'),
ordered = TRUE)
print(levels(zeno_pws_df$edss_severity_cat))## [1] "mild" "moderate" "severe"print(table(zeno_pws_df$edss_severity_cat))##
## mild moderate severe
## 99 81 44missing variables in each column
colSums(is.na(zeno_pws_df))## X
## 0
## video_id_date_name_pose_zv
## 0
## id_date_pose_zv
## 0
## task_pose_zv
## 0
## frames_per_second_pose_zv
## 0
## total_video_duration_sec_pose_zv
## 0
## delta_pix_h_rel_median_pose_zv
## 16
## walking_segmets_n_pose_zv
## 40
## walking_segments_duration_mean_pose_zv
## 40
## walking_segments_duration_median_pose_zv
## 40
## stride_time_mean_sec_pose_zv
## 48
## stride_time_median_sec_pose_zv
## 48
## stride_time_std_pose_zv
## 53
## stride_time_cv_pose_zv
## 53
## stride_time_max_pose_zv
## 48
## stride_time_min_pose_zv
## 48
## mean_cadence_step_per_min_pose_zv
## 40
## stride_width_mean_cm_pose_zv
## 40
## stride_width_median_cm_pose_zv
## 40
## stride_width_std_pose_zv
## 42
## stride_width_cv_pose_zv
## 42
## stride_width_max_pose_zv
## 40
## stride_width_min_pose_zv
## 40
## foot1_gait_cycle_time_mean_pose_zv
## 163
## foot1_stance_time_mean_pose_zv
## 163
## foot1_stance_per_mean_pose_zv
## 163
## foot1_swing_time_mean_pose_zv
## 163
## foot1_swing_per_mean_pose_zv
## 163
## foot1_ini_double_support_time_mean_pose_zv
## 163
## foot1_term_double_support_time_mean_pose_zv
## 163
## foot1_tot_double_support_time_mean_pose_zv
## 163
## foot1_double_support_per_mean_pose_zv
## 163
## foot1_single_support_time_mean_pose_zv
## 163
## foot1_single_support_per_mean_pose_zv
## 163
## walk_segment_pose_zv
## 163
## foot1_pose_zv
## 0
## id_video
## 0
## visit_date_video
## 0
## bw_id
## 0
## trialdate
## 0
## visit_date
## 0
## demoGait_dateDiff
## 0
## demoEHR_DiseaseDuration
## 0
## clean_sex
## 0
## clean_race
## 0
## clean_ethnicity
## 0
## demoEHR_Age
## 0
## bingoEHR_DX_MS.DX
## 0
## bingoEHR_EDSS_measure_value
## 0
## msfcEHR_T25FW.SPEED.AVG
## 0
## FW_cadencestepsminmean
## 0
## FW_singlesupportmean
## 0
## FW_singlesupportratiolr
## 0
## FW_stridetimeseccv
## 0
## FW_stridetimesecmean
## 0
## FW_stridewidthcmmean
## 0
## FW_stridewidthcmsd
## 0
## FW_totaldsupportmean
## 0
## FW_totaldsupportratiolr
## 0
## FW_velocitycmsecmean
## 0
## PWS_cadencestepsminmean
## 0
## PWS_singlesupportmean
## 0
## PWS_singlesupportratiolr
## 0
## PWS_stridetimeseccv
## 0
## PWS_stridetimesecmean
## 0
## PWS_stridewidthcmmean
## 0
## PWS_stridewidthcmsd
## 0
## PWS_totaldsupportmean
## 0
## PWS_totaldsupportratiolr
## 0
## PWS_velocitycmsecmean
## 0
## redcap_event_name
## 0
## demographic_diagnosis
## 0
## msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff
## 0
## msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff
## 31
## demoEHR_DX_dateDiff
## 0
## demoEHR_EDSS_dateDiff
## 0
## bingoEHR_Vitals_height.in.
## 13
## demoEHR_Vitals_dateDiff
## 0
## bingoEHR_first_MS.DX
## 0
## edss_severity_num
## 0
## edss_severity_cat
## 0
## t25fw_group_num
## 0
## t25fw_group_cat
## 0
## race_ethnicity_clean
## 0
## ms_dx_condensed
## 0Visit types
table(zeno_pws_df$redcap_event_name)##
## Brainwalk: Baseline visit (Arm 1: Baseline visit)
## 138
## Year 2 Visit (Arm 1: Baseline visit)
## 78
## Year 3 Visit (Arm 1: Baseline visit)
## 7
## Year 4 Visit (Arm 1: Baseline visit)
## 1Load and format FW data
All videos included in analysis.
Each row = 1 video
zeno_fw_path <- file.path("C:/Users/mmccu/Box/MM_Personal/5_Projects/BoveLab/3_Data_and_Code/gait_bw_zeno_home_analysis",
analysis_version,
"000_merged_cleaned_data",
"zv_bw_merged_gait_vertical_FW_1_clean.csv")
zeno_fw_df <- read.csv(zeno_fw_path)
table(zeno_fw_df$task_pose)##
## gait_vertical_FW_1
## 222Factor categorical vars
str(zeno_fw_df)## 'data.frame': 222 obs. of 85 variables:
## $ X : int 0 1 3 4 5 6 8 9 11 12 ...
## $ video_id_date_name_pose_zv : chr "gait_vertical_FW_1_BW-0002_2022_09_12" "gait_vertical_FW_1_BW-0002_2023_09_12" "gait_vertical_FW_1_BW-0004_2022_09_19" "gait_vertical_FW_1_BW-0006_2022_09_26" ...
## $ id_date_pose_zv : chr "BW-0002\\2022_09_12" "BW-0002\\2023_09_12" "BW-0004\\2022_09_19" "BW-0006\\2022_09_26" ...
## $ task_pose_zv : chr "gait_vertical_FW_1" "gait_vertical_FW_1" "gait_vertical_FW_1" "gait_vertical_FW_1" ...
## $ frames_per_second_pose_zv : int 25 30 30 30 30 30 30 30 30 30 ...
## $ total_video_duration_sec_pose_zv : num 29 36.5 25.3 25 22.6 ...
## $ delta_pix_h_rel_median_pose_zv : num 0.28 0.25 0.36 0.27 0.43 0.23 0.44 0.35 0.44 0.29 ...
## $ walking_segmets_n_pose_zv : num NA 2 2 1 4 4 3 3 2 2 ...
## $ walking_segments_duration_mean_pose_zv : num NA 5.23 2.47 4.87 3.48 4.79 2.38 4.44 4.63 4.27 ...
## $ walking_segments_duration_median_pose_zv : num NA 5.23 2.47 4.87 3.47 4.97 2.1 4.4 4.63 4.27 ...
## $ stride_time_mean_sec_pose_zv : num NA 1.007 1.433 0.957 1.027 ...
## $ stride_time_median_sec_pose_zv : num NA 1.067 1.333 0.967 0.933 ...
## $ stride_time_std_pose_zv : num NA 0.266 0.233 0.037 0.189 0.126 0.052 0.101 0.066 0.055 ...
## $ stride_time_cv_pose_zv : num NA 26.44 16.28 3.88 18.38 ...
## $ stride_time_max_pose_zv : num NA 1.27 1.7 1 1.5 ...
## $ stride_time_min_pose_zv : num NA 0.433 1.267 0.9 0.7 ...
## $ mean_cadence_step_per_min_pose_zv : num NA 120 110 123 117 ...
## $ stride_width_mean_cm_pose_zv : num NA 13.75 8.97 15.99 10.74 ...
## $ stride_width_median_cm_pose_zv : num NA 14.28 8.87 15.02 11.1 ...
## $ stride_width_std_pose_zv : num NA 5.39 5.71 4.27 4.49 ...
## $ stride_width_cv_pose_zv : num NA 39.2 63.7 26.7 41.8 ...
## $ stride_width_max_pose_zv : num NA 22.6 17.4 23.1 17.2 ...
## $ stride_width_min_pose_zv : num NA 1.69 2.53 11.27 2.33 ...
## $ foot1_gait_cycle_time_mean_pose_zv : num NA NA 1 NA 0.8 0.867 NA NA NA NA ...
## $ foot1_stance_time_mean_pose_zv : num NA NA 0.533 NA 0.5 0.567 NA NA NA NA ...
## $ foot1_stance_per_mean_pose_zv : num NA NA 53.3 NA 62.5 ...
## $ foot1_swing_time_mean_pose_zv : num NA NA 0.467 NA 0.3 0.3 NA NA NA NA ...
## $ foot1_swing_per_mean_pose_zv : num NA NA 46.7 NA 37.5 ...
## $ foot1_ini_double_support_time_mean_pose_zv : num NA NA 0.1 NA 0.033 0.067 NA NA NA NA ...
## $ foot1_term_double_support_time_mean_pose_zv: num NA NA 0.1 NA 0.033 0.167 NA NA NA NA ...
## $ foot1_tot_double_support_time_mean_pose_zv : num NA NA 0.2 NA 0.067 0.233 NA NA NA NA ...
## $ foot1_double_support_per_mean_pose_zv : num NA NA 20 NA 8.33 ...
## $ foot1_single_support_time_mean_pose_zv : num NA NA 0.333 NA 0.433 0.333 NA NA NA NA ...
## $ foot1_single_support_per_mean_pose_zv : num NA NA 33.3 NA 54.2 ...
## $ walk_segment_pose_zv : num NA NA 0 NA 0 2 NA NA NA NA ...
## $ foot1_pose_zv : chr "" "" "left" "" ...
## $ id_video : chr "BW-0002" "BW-0002" "BW-0004" "BW-0006" ...
## $ visit_date_video : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ bw_id : chr "BW-0002" "BW-0002" "BW-0004" "BW-0006" ...
## $ trialdate : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ visit_date : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ demoGait_dateDiff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DiseaseDuration : num 0 1 17 17 7 8 25 26 30 31 ...
## $ clean_sex : chr "Male" "Male" "Female" "Female" ...
## $ clean_race : chr "Other" "Other" "White" "White" ...
## $ clean_ethnicity : chr "Hispanic Or Latino" "Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" ...
## $ demoEHR_Age : num 37 37 46 65 53 53 60 60 71 71 ...
## $ bingoEHR_DX_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "PPMS (Primary-progressive Multiple Sclerosis)" ...
## $ bingoEHR_EDSS_measure_value : num 3.5 3 3 3 3 2 3 3 3.5 3.5 ...
## $ msfcEHR_T25FW.SPEED.AVG : num 5 6.1 4.3 4.6 4.6 4.9 3.7 4.4 5.2 6.1 ...
## $ FW_cadencestepsminmean : num 113 103 134 120 124 ...
## $ FW_singlesupportmean : num 36.8 34.7 39.8 36.5 38.1 ...
## $ FW_singlesupportratiolr : num 1.09 1.11 1.02 1.07 1.01 ...
## $ FW_stridetimeseccv : num 4.01 5.92 3.1 3.12 3.77 ...
## $ FW_stridetimesecmean : num 1.061 1.158 0.891 0.99 0.959 ...
## $ FW_stridewidthcmmean : num 14.76 13.91 7.1 7.55 11.94 ...
## $ FW_stridewidthcmsd : num 4.97 3.75 4.08 3.19 3.08 ...
## $ FW_totaldsupportmean : num 27.3 30.5 20.4 26.9 24 ...
## $ FW_totaldsupportratiolr : num 0.985 1.003 0.992 0.984 1.046 ...
## $ FW_velocitycmsecmean : num 145 119 197 133 164 ...
## $ PWS_cadencestepsminmean : num 79.4 74.6 100.2 107.3 90.2 ...
## $ PWS_singlesupportmean : num 30.5 30.8 37.1 34.8 32.6 ...
## $ PWS_singlesupportratiolr : num 1.2 1.07 1.14 1.1 1.11 ...
## $ PWS_stridetimeseccv : num 24.5 21.4 23.5 15.5 36.1 ...
## $ PWS_stridetimesecmean : num 1.51 1.61 1.19 1.12 1.33 ...
## $ PWS_stridewidthcmmean : num 16.63 12.11 2.31 8.48 11.43 ...
## $ PWS_stridewidthcmsd : num 11.47 5.32 7.82 16.29 13.71 ...
## $ PWS_totaldsupportmean : num 35.4 35.2 36.4 30.4 30.1 ...
## $ PWS_totaldsupportratiolr : num 1.091 0.912 1.216 0.995 1.077 ...
## $ PWS_velocitycmsecmean : num 66.1 74.1 101.2 94.7 78 ...
## $ redcap_event_name : chr "Brainwalk: Baseline visit (Arm 1: Baseline visit)" "Year 2 Visit (Arm 1: Baseline visit)" "Brainwalk: Baseline visit (Arm 1: Baseline visit)" "Brainwalk: Baseline visit (Arm 1: Baseline visit)" ...
## $ demographic_diagnosis : chr "MS" "MS" "MS" "MS" ...
## $ msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff : num 0 0 0 NA 0 0 0 0 0 0 ...
## $ demoEHR_DX_dateDiff : num 97 462 6289 6296 2645 ...
## $ demoEHR_EDSS_dateDiff : num 0 0 0 0 0 440 0 0 0 -175 ...
## $ bingoEHR_Vitals_height.in. : num 66 NA 67.5 67 NA 68 65 66 63 63 ...
## $ demoEHR_Vitals_dateDiff : num -0.454 -11.391 -0.453 -0.429 -0.552 ...
## $ bingoEHR_first_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "PPMS (Primary-progressive Multiple Sclerosis)" ...
## $ edss_severity_num : num 2 2 2 2 2 1 2 2 2 2 ...
## $ edss_severity_cat : chr "moderate" "moderate" "moderate" "moderate" ...
## $ t25fw_group_num : num 1 2 1 1 1 1 1 1 1 2 ...
## $ t25fw_group_cat : chr "under_6" "6_to_8" "under_6" "under_6" ...
## $ race_ethnicity_clean : chr "Hispanic or Latino" "Hispanic or Latino" "White Not Hispanic" "White Not Hispanic" ...
## $ ms_dx_condensed : chr "RRMS" "RRMS" "RRMS" "Progressive MS" ...zeno_fw_df <- zeno_fw_df %>%
mutate_at(c("video_id_date_name_pose_zv", "id_date_pose_zv", "task_pose_zv",
"id_video", "bw_id", "clean_sex", "bingoEHR_DX_MS.DX",
"demographic_diagnosis", "race_ethnicity_clean",
"race_ethnicity_clean", "ms_dx_condensed",
"redcap_event_name", "bingoEHR_EDSS_measure_value"),
as.factor)
str(zeno_fw_df)## 'data.frame': 222 obs. of 85 variables:
## $ X : int 0 1 3 4 5 6 8 9 11 12 ...
## $ video_id_date_name_pose_zv : Factor w/ 222 levels "gait_vertical_FW_1_BW-0002_2022_09_12",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ id_date_pose_zv : Factor w/ 222 levels "BW-0002\\2022_09_12",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ task_pose_zv : Factor w/ 1 level "gait_vertical_FW_1": 1 1 1 1 1 1 1 1 1 1 ...
## $ frames_per_second_pose_zv : int 25 30 30 30 30 30 30 30 30 30 ...
## $ total_video_duration_sec_pose_zv : num 29 36.5 25.3 25 22.6 ...
## $ delta_pix_h_rel_median_pose_zv : num 0.28 0.25 0.36 0.27 0.43 0.23 0.44 0.35 0.44 0.29 ...
## $ walking_segmets_n_pose_zv : num NA 2 2 1 4 4 3 3 2 2 ...
## $ walking_segments_duration_mean_pose_zv : num NA 5.23 2.47 4.87 3.48 4.79 2.38 4.44 4.63 4.27 ...
## $ walking_segments_duration_median_pose_zv : num NA 5.23 2.47 4.87 3.47 4.97 2.1 4.4 4.63 4.27 ...
## $ stride_time_mean_sec_pose_zv : num NA 1.007 1.433 0.957 1.027 ...
## $ stride_time_median_sec_pose_zv : num NA 1.067 1.333 0.967 0.933 ...
## $ stride_time_std_pose_zv : num NA 0.266 0.233 0.037 0.189 0.126 0.052 0.101 0.066 0.055 ...
## $ stride_time_cv_pose_zv : num NA 26.44 16.28 3.88 18.38 ...
## $ stride_time_max_pose_zv : num NA 1.27 1.7 1 1.5 ...
## $ stride_time_min_pose_zv : num NA 0.433 1.267 0.9 0.7 ...
## $ mean_cadence_step_per_min_pose_zv : num NA 120 110 123 117 ...
## $ stride_width_mean_cm_pose_zv : num NA 13.75 8.97 15.99 10.74 ...
## $ stride_width_median_cm_pose_zv : num NA 14.28 8.87 15.02 11.1 ...
## $ stride_width_std_pose_zv : num NA 5.39 5.71 4.27 4.49 ...
## $ stride_width_cv_pose_zv : num NA 39.2 63.7 26.7 41.8 ...
## $ stride_width_max_pose_zv : num NA 22.6 17.4 23.1 17.2 ...
## $ stride_width_min_pose_zv : num NA 1.69 2.53 11.27 2.33 ...
## $ foot1_gait_cycle_time_mean_pose_zv : num NA NA 1 NA 0.8 0.867 NA NA NA NA ...
## $ foot1_stance_time_mean_pose_zv : num NA NA 0.533 NA 0.5 0.567 NA NA NA NA ...
## $ foot1_stance_per_mean_pose_zv : num NA NA 53.3 NA 62.5 ...
## $ foot1_swing_time_mean_pose_zv : num NA NA 0.467 NA 0.3 0.3 NA NA NA NA ...
## $ foot1_swing_per_mean_pose_zv : num NA NA 46.7 NA 37.5 ...
## $ foot1_ini_double_support_time_mean_pose_zv : num NA NA 0.1 NA 0.033 0.067 NA NA NA NA ...
## $ foot1_term_double_support_time_mean_pose_zv: num NA NA 0.1 NA 0.033 0.167 NA NA NA NA ...
## $ foot1_tot_double_support_time_mean_pose_zv : num NA NA 0.2 NA 0.067 0.233 NA NA NA NA ...
## $ foot1_double_support_per_mean_pose_zv : num NA NA 20 NA 8.33 ...
## $ foot1_single_support_time_mean_pose_zv : num NA NA 0.333 NA 0.433 0.333 NA NA NA NA ...
## $ foot1_single_support_per_mean_pose_zv : num NA NA 33.3 NA 54.2 ...
## $ walk_segment_pose_zv : num NA NA 0 NA 0 2 NA NA NA NA ...
## $ foot1_pose_zv : chr "" "" "left" "" ...
## $ id_video : Factor w/ 154 levels "BW-0002","BW-0004",..: 1 1 2 3 4 4 5 5 6 6 ...
## $ visit_date_video : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ bw_id : Factor w/ 154 levels "BW-0002","BW-0004",..: 1 1 2 3 4 4 5 5 6 6 ...
## $ trialdate : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ visit_date : chr "2022-09-12" "2023-09-12" "2022-09-19" "2022-09-26" ...
## $ demoGait_dateDiff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DiseaseDuration : num 0 1 17 17 7 8 25 26 30 31 ...
## $ clean_sex : Factor w/ 3 levels "Female","Male",..: 2 2 1 1 1 1 2 2 1 1 ...
## $ clean_race : chr "Other" "Other" "White" "White" ...
## $ clean_ethnicity : chr "Hispanic Or Latino" "Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" ...
## $ demoEHR_Age : num 37 37 46 65 53 53 60 60 71 71 ...
## $ bingoEHR_DX_MS.DX : Factor w/ 5 levels "MS, Subtype Not Specified",..: 4 4 4 2 4 4 5 5 4 4 ...
## $ bingoEHR_EDSS_measure_value : Factor w/ 13 levels "0","1","1.5",..: 7 6 6 6 6 4 6 6 7 7 ...
## $ msfcEHR_T25FW.SPEED.AVG : num 5 6.1 4.3 4.6 4.6 4.9 3.7 4.4 5.2 6.1 ...
## $ FW_cadencestepsminmean : num 113 103 134 120 124 ...
## $ FW_singlesupportmean : num 36.8 34.7 39.8 36.5 38.1 ...
## $ FW_singlesupportratiolr : num 1.09 1.11 1.02 1.07 1.01 ...
## $ FW_stridetimeseccv : num 4.01 5.92 3.1 3.12 3.77 ...
## $ FW_stridetimesecmean : num 1.061 1.158 0.891 0.99 0.959 ...
## $ FW_stridewidthcmmean : num 14.76 13.91 7.1 7.55 11.94 ...
## $ FW_stridewidthcmsd : num 4.97 3.75 4.08 3.19 3.08 ...
## $ FW_totaldsupportmean : num 27.3 30.5 20.4 26.9 24 ...
## $ FW_totaldsupportratiolr : num 0.985 1.003 0.992 0.984 1.046 ...
## $ FW_velocitycmsecmean : num 145 119 197 133 164 ...
## $ PWS_cadencestepsminmean : num 79.4 74.6 100.2 107.3 90.2 ...
## $ PWS_singlesupportmean : num 30.5 30.8 37.1 34.8 32.6 ...
## $ PWS_singlesupportratiolr : num 1.2 1.07 1.14 1.1 1.11 ...
## $ PWS_stridetimeseccv : num 24.5 21.4 23.5 15.5 36.1 ...
## $ PWS_stridetimesecmean : num 1.51 1.61 1.19 1.12 1.33 ...
## $ PWS_stridewidthcmmean : num 16.63 12.11 2.31 8.48 11.43 ...
## $ PWS_stridewidthcmsd : num 11.47 5.32 7.82 16.29 13.71 ...
## $ PWS_totaldsupportmean : num 35.4 35.2 36.4 30.4 30.1 ...
## $ PWS_totaldsupportratiolr : num 1.091 0.912 1.216 0.995 1.077 ...
## $ PWS_velocitycmsecmean : num 66.1 74.1 101.2 94.7 78 ...
## $ redcap_event_name : Factor w/ 4 levels "Brainwalk: Baseline visit (Arm 1: Baseline visit)",..: 1 2 1 1 1 2 2 3 1 2 ...
## $ demographic_diagnosis : Factor w/ 1 level "MS": 1 1 1 1 1 1 1 1 1 1 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff : num 0 0 0 NA 0 0 0 0 0 0 ...
## $ demoEHR_DX_dateDiff : num 97 462 6289 6296 2645 ...
## $ demoEHR_EDSS_dateDiff : num 0 0 0 0 0 440 0 0 0 -175 ...
## $ bingoEHR_Vitals_height.in. : num 66 NA 67.5 67 NA 68 65 66 63 63 ...
## $ demoEHR_Vitals_dateDiff : num -0.454 -11.391 -0.453 -0.429 -0.552 ...
## $ bingoEHR_first_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "PPMS (Primary-progressive Multiple Sclerosis)" ...
## $ edss_severity_num : num 2 2 2 2 2 1 2 2 2 2 ...
## $ edss_severity_cat : chr "moderate" "moderate" "moderate" "moderate" ...
## $ t25fw_group_num : num 1 2 1 1 1 1 1 1 1 2 ...
## $ t25fw_group_cat : chr "under_6" "6_to_8" "under_6" "under_6" ...
## $ race_ethnicity_clean : Factor w/ 5 levels "Asian","Black Or African American",..: 3 3 5 5 5 5 3 3 5 5 ...
## $ ms_dx_condensed : Factor w/ 3 levels "MS, Subtype Not Specified",..: 3 3 3 2 3 3 2 2 3 3 ...# assign levels to categorical variables
table(zeno_fw_df$race_ethnicity_clean)##
## Asian Black Or African American Hispanic or Latino
## 17 14 20
## Other/Unknown/Declined White Not Hispanic
## 18 153zeno_fw_df$race_ethnicity_clean <- factor(zeno_fw_df$race_ethnicity_clean,
levels = c('White Not Hispanic',
'Asian',
'Black Or African American',
'Hispanic or Latino',
'Other/Unknown/Declined'),
ordered = FALSE)
print(levels(zeno_fw_df$race_ethnicity_clean))## [1] "White Not Hispanic" "Asian"
## [3] "Black Or African American" "Hispanic or Latino"
## [5] "Other/Unknown/Declined"table(zeno_fw_df$ms_dx_condensed)##
## MS, Subtype Not Specified Progressive MS RRMS
## 2 39 181zeno_fw_df$ms_dx_condensed <- factor(zeno_fw_df$ms_dx_condensed,
levels = c('RRMS',
'Progressive MS',
'MS, Subtype Not Specified'),
ordered = FALSE)
print(levels(zeno_fw_df$ms_dx_condensed))## [1] "RRMS" "Progressive MS"
## [3] "MS, Subtype Not Specified"table(zeno_fw_df$clean_sex)##
## Female Male Non-Binary
## 167 53 2zeno_fw_df$clean_sex <- factor(zeno_fw_df$clean_sex,
levels = c('Female',
'Male',
'Non-Binary'),
ordered = FALSE)
print(levels(zeno_fw_df$clean_sex))## [1] "Female" "Male" "Non-Binary"table(zeno_fw_df$edss_severity_cat)##
## mild moderate severe
## 99 82 41zeno_fw_df$edss_severity_cat <- factor(zeno_fw_df$edss_severity_cat,
levels = c('mild',
'moderate',
'severe'),
ordered = TRUE)
print(levels(zeno_fw_df$edss_severity_cat))## [1] "mild" "moderate" "severe"Missing variables per column
colSums(is.na(zeno_fw_df))## X
## 0
## video_id_date_name_pose_zv
## 0
## id_date_pose_zv
## 0
## task_pose_zv
## 0
## frames_per_second_pose_zv
## 0
## total_video_duration_sec_pose_zv
## 0
## delta_pix_h_rel_median_pose_zv
## 3
## walking_segmets_n_pose_zv
## 46
## walking_segments_duration_mean_pose_zv
## 46
## walking_segments_duration_median_pose_zv
## 46
## stride_time_mean_sec_pose_zv
## 53
## stride_time_median_sec_pose_zv
## 53
## stride_time_std_pose_zv
## 54
## stride_time_cv_pose_zv
## 54
## stride_time_max_pose_zv
## 53
## stride_time_min_pose_zv
## 53
## mean_cadence_step_per_min_pose_zv
## 46
## stride_width_mean_cm_pose_zv
## 47
## stride_width_median_cm_pose_zv
## 47
## stride_width_std_pose_zv
## 49
## stride_width_cv_pose_zv
## 49
## stride_width_max_pose_zv
## 47
## stride_width_min_pose_zv
## 47
## foot1_gait_cycle_time_mean_pose_zv
## 188
## foot1_stance_time_mean_pose_zv
## 188
## foot1_stance_per_mean_pose_zv
## 188
## foot1_swing_time_mean_pose_zv
## 188
## foot1_swing_per_mean_pose_zv
## 188
## foot1_ini_double_support_time_mean_pose_zv
## 188
## foot1_term_double_support_time_mean_pose_zv
## 188
## foot1_tot_double_support_time_mean_pose_zv
## 188
## foot1_double_support_per_mean_pose_zv
## 188
## foot1_single_support_time_mean_pose_zv
## 188
## foot1_single_support_per_mean_pose_zv
## 188
## walk_segment_pose_zv
## 188
## foot1_pose_zv
## 0
## id_video
## 0
## visit_date_video
## 0
## bw_id
## 0
## trialdate
## 0
## visit_date
## 0
## demoGait_dateDiff
## 0
## demoEHR_DiseaseDuration
## 0
## clean_sex
## 0
## clean_race
## 0
## clean_ethnicity
## 0
## demoEHR_Age
## 0
## bingoEHR_DX_MS.DX
## 0
## bingoEHR_EDSS_measure_value
## 0
## msfcEHR_T25FW.SPEED.AVG
## 0
## FW_cadencestepsminmean
## 0
## FW_singlesupportmean
## 0
## FW_singlesupportratiolr
## 0
## FW_stridetimeseccv
## 0
## FW_stridetimesecmean
## 0
## FW_stridewidthcmmean
## 0
## FW_stridewidthcmsd
## 0
## FW_totaldsupportmean
## 0
## FW_totaldsupportratiolr
## 0
## FW_velocitycmsecmean
## 0
## PWS_cadencestepsminmean
## 0
## PWS_singlesupportmean
## 0
## PWS_singlesupportratiolr
## 0
## PWS_stridetimeseccv
## 0
## PWS_stridetimesecmean
## 0
## PWS_stridewidthcmmean
## 0
## PWS_stridewidthcmsd
## 0
## PWS_totaldsupportmean
## 0
## PWS_totaldsupportratiolr
## 0
## PWS_velocitycmsecmean
## 0
## redcap_event_name
## 0
## demographic_diagnosis
## 0
## msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff
## 0
## msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff
## 31
## demoEHR_DX_dateDiff
## 0
## demoEHR_EDSS_dateDiff
## 0
## bingoEHR_Vitals_height.in.
## 13
## demoEHR_Vitals_dateDiff
## 0
## bingoEHR_first_MS.DX
## 0
## edss_severity_num
## 0
## edss_severity_cat
## 0
## t25fw_group_num
## 0
## t25fw_group_cat
## 0
## race_ethnicity_clean
## 0
## ms_dx_condensed
## 0Visit types
table(zeno_fw_df$redcap_event_name)##
## Brainwalk: Baseline visit (Arm 1: Baseline visit)
## 139
## Year 2 Visit (Arm 1: Baseline visit)
## 75
## Year 3 Visit (Arm 1: Baseline visit)
## 7
## Year 4 Visit (Arm 1: Baseline visit)
## 1Load and format Home video data
home_path <- file.path("C:/Users/mmccu/Box/MM_Personal/5_Projects/BoveLab/3_Data_and_Code/gait_bw_zeno_home_analysis",
analysis_version,
"000_merged_cleaned_data",
"hv_bw_merged_clean.csv")
home_df <- read.csv(home_path)
nrow(home_df)## [1] 65table(home_df$demographic_diagnosis)##
## MS
## 65table(home_df$task_pose_hv)##
## gait_vertical_left gait_vertical_right
## 32 33Factor categorical vars
str(home_df)## 'data.frame': 65 obs. of 86 variables:
## $ X : int 0 1 2 3 4 5 6 8 10 11 ...
## $ video_id_date_name_pose_hv : chr "gait_vertical_left_BW-0018_10-24-23" "gait_vertical_right_BW-0018_10-24-23" "gait_vertical_left_BW-0023_05-05-23" "gait_vertical_right_BW-0023_05-05-23" ...
## $ id_date_pose_hv : chr "BW-0018\\10-24-23" "BW-0018\\10-24-23" "BW-0023\\05-05-23" "BW-0023\\05-05-23" ...
## $ task_pose_hv : chr "gait_vertical_left" "gait_vertical_right" "gait_vertical_left" "gait_vertical_right" ...
## $ frames_per_second_pose_hv : int 30 30 30 30 30 30 30 30 30 30 ...
## $ total_video_duration_sec_pose_hv : num 26.8 24.4 36 38.5 36.1 ...
## $ delta_pix_h_rel_median_pose_hv : num 0.22 0.31 0.43 0.39 0.3 0.39 0.03 0.08 0.22 0.24 ...
## $ walking_segmets_n_pose_hv : num 1 1 4 5 3 2 NA NA 5 5 ...
## $ walking_segments_duration_mean_pose_hv : num 3.17 2.4 4.42 4.78 4.19 5.07 NA NA 4.47 3.77 ...
## $ walking_segments_duration_median_pose_hv : num 3.17 2.4 4.33 4.63 3.83 5.07 NA NA 4.3 3.7 ...
## $ stride_time_mean_sec_pose_hv : num 1.008 NA 1.027 1.07 0.998 ...
## $ stride_time_median_sec_pose_hv : num 1.05 NA 1.033 1.033 0.983 ...
## $ stride_time_std_pose_hv : num 0.171 NA 0.066 0.098 0.087 0.076 NA NA 0.229 0.078 ...
## $ stride_time_cv_pose_hv : num 16.94 NA 6.46 9.11 8.74 ...
## $ stride_time_max_pose_hv : num 1.17 NA 1.17 1.3 1.1 ...
## $ stride_time_min_pose_hv : num 0.767 NA 0.833 0.933 0.867 ...
## $ mean_cadence_step_per_min_pose_hv : num 133 75 113 106 111 ...
## $ stride_width_mean_cm_pose_hv : num 12.81 8.21 10.59 10.62 9.89 ...
## $ stride_width_median_cm_pose_hv : num 13.34 8.21 10.69 10.86 9.13 ...
## $ stride_width_std_pose_hv : num 3.54 10.46 3.52 3.6 3.19 ...
## $ stride_width_cv_pose_hv : num 27.6 127.4 33.3 33.9 32.3 ...
## $ stride_width_max_pose_hv : num 17.4 15.6 19.6 17.9 14.8 ...
## $ stride_width_min_pose_hv : num 7.921 0.813 4.529 0.624 4.146 ...
## $ foot1_gait_cycle_time_mean_pose_hv : num 1.3 NA NA NA NA ...
## $ foot1_stance_time_mean_pose_hv : num 0.9 NA NA NA NA ...
## $ foot1_stance_per_mean_pose_hv : num 69.2 NA NA NA NA ...
## $ foot1_swing_time_mean_pose_hv : num 0.4 NA NA NA NA 0.433 NA NA 0.333 0.433 ...
## $ foot1_swing_per_mean_pose_hv : num 30.8 NA NA NA NA ...
## $ foot1_ini_double_support_time_mean_pose_hv : num 0.4 NA NA NA NA 0.433 NA NA 0.5 0.333 ...
## $ foot1_term_double_support_time_mean_pose_hv: num 0.2 NA NA NA NA 0.033 NA NA 0.033 0.433 ...
## $ foot1_tot_double_support_time_mean_pose_hv : num 0.6 NA NA NA NA 0.467 NA NA 0.533 0.767 ...
## $ foot1_double_support_per_mean_pose_hv : num 46.2 NA NA NA NA ...
## $ foot1_single_support_time_mean_pose_hv : num 0.3 NA NA NA NA 0.467 NA NA 0.6 0.333 ...
## $ foot1_single_support_per_mean_pose_hv : num 23.1 NA NA NA NA ...
## $ walk_segment_pose_hv : num 0 NA NA NA NA 1 NA NA 1 1 ...
## $ foot1_pose_hv : chr "right" "" "" "" ...
## $ id_video : chr "BW-0018" "BW-0018" "BW-0023" "BW-0023" ...
## $ visit_date_video : chr "2023-10-24" "2023-10-24" "2023-05-05" "2023-05-05" ...
## $ bw_id : chr "BW-0018" "BW-0018" "BW-0023" "BW-0023" ...
## $ trialdate : chr "2023-10-24" "2023-10-24" "2023-04-26" "2023-04-26" ...
## $ visit_date : chr "2023-10-24" "2023-10-24" "2023-04-26" "2023-04-26" ...
## $ demoGait_dateDiff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DiseaseDuration : num 4 4 21 21 22 22 14 14 7 7 ...
## $ clean_sex : chr "Male" "Male" "Female" "Female" ...
## $ clean_race : chr "White" "White" "Other" "Other" ...
## $ clean_ethnicity : chr "Not Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" ...
## $ demoEHR_Age : num 57 57 40 40 40 40 35 35 64 64 ...
## $ bingoEHR_DX_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" ...
## $ bingoEHR_EDSS_measure_value : num 3 3 3.5 3.5 2.5 2.5 4.5 4.5 6.5 6.5 ...
## $ msfcEHR_T25FW.SPEED.AVG : num 3.6 3.6 3.4 3.4 4.4 4.4 4.4 4.4 6.6 6.6 ...
## $ FW_cadencestepsminmean : num 121 121 139 139 151 ...
## $ FW_singlesupportmean : num 32.5 32.5 38.9 38.9 39.9 ...
## $ FW_singlesupportratiolr : num 0.816 0.816 1.017 1.017 0.992 ...
## $ FW_stridetimeseccv : num 36.37 36.37 5.61 5.61 14.06 ...
## $ FW_stridetimesecmean : num 0.999 0.999 0.858 0.858 0.806 ...
## $ FW_stridewidthcmmean : num 5.55 5.55 6.85 6.85 9.31 ...
## $ FW_stridewidthcmsd : num 22.62 22.62 2.3 2.3 3.12 ...
## $ FW_totaldsupportmean : num 25.6 25.6 21.8 21.8 19.5 ...
## $ FW_totaldsupportratiolr : num 0.845 0.845 0.983 0.983 0.995 ...
## $ FW_velocitycmsecmean : num 123 123 190 190 205 ...
## $ PWS_cadencestepsminmean : num 116 116 112 112 127 ...
## $ PWS_singlesupportmean : num 34.8 34.8 35.8 35.8 36.9 ...
## $ PWS_singlesupportratiolr : num 0.952 0.952 1.038 1.038 1.02 ...
## $ PWS_stridetimeseccv : num 3.49 3.49 6.24 6.24 5.56 ...
## $ PWS_stridetimesecmean : num 1.028 1.028 1.065 1.065 0.945 ...
## $ PWS_stridewidthcmmean : num 8.19 8.19 8.33 8.33 8.69 ...
## $ PWS_stridewidthcmsd : num 3.72 3.72 2.38 2.38 1.95 ...
## $ PWS_totaldsupportmean : num 30.3 30.3 28.5 28.5 26.1 ...
## $ PWS_totaldsupportratiolr : num 1.01 1.01 0.98 0.98 1 ...
## $ PWS_velocitycmsecmean : num 137 137 116 116 140 ...
## $ redcap_event_name : chr "Year 2 Visit (Arm 1: Baseline visit)" "Year 2 Visit (Arm 1: Baseline visit)" "Year 2 Visit (Arm 1: Baseline visit)" "Year 2 Visit (Arm 1: Baseline visit)" ...
## $ demographic_diagnosis : chr "MS" "MS" "MS" "MS" ...
## $ msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DX_dateDiff : num 1617 1617 7969 7969 8149 ...
## $ demoEHR_EDSS_dateDiff : num 0 0 16 16 -182 -182 0 0 -111 -111 ...
## $ bingoEHR_Vitals_height.in. : num 67 67 66 66 66 66 66 66 67 67 ...
## $ demoEHR_Vitals_dateDiff : num -0.347 -0.347 15.553 15.553 -0.561 ...
## $ bingoEHR_first_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" ...
## $ edss_severity_num : num 2 2 2 2 2 2 3 3 3 3 ...
## $ edss_severity_cat : chr "moderate" "moderate" "moderate" "moderate" ...
## $ t25fw_group_num : num 1 1 1 1 1 1 1 1 2 2 ...
## $ t25fw_group_cat : chr "under_6" "under_6" "under_6" "under_6" ...
## $ race_ethnicity_clean : chr "White Not Hispanic" "White Not Hispanic" "Other/Unknown/Declined" "Other/Unknown/Declined" ...
## $ ms_dx_condensed : chr "RRMS" "RRMS" "RRMS" "RRMS" ...
## $ bw_hv_date_diff_days : int 0 0 9 9 0 0 -1 -1 -1 -1 ...home_df <- home_df %>%
mutate_at(c("video_id_date_name_pose_hv", "id_date_pose_hv", "task_pose_hv",
"id_video", "bw_id", "clean_sex", "bingoEHR_DX_MS.DX",
"demographic_diagnosis", "race_ethnicity_clean",
"race_ethnicity_clean", "ms_dx_condensed",
"redcap_event_name", "bingoEHR_EDSS_measure_value"),
as.factor)
str(home_df)## 'data.frame': 65 obs. of 86 variables:
## $ X : int 0 1 2 3 4 5 6 8 10 11 ...
## $ video_id_date_name_pose_hv : Factor w/ 65 levels "gait_vertical_left_BW-0018_10-24-23",..: 1 33 2 34 3 35 4 36 5 37 ...
## $ id_date_pose_hv : Factor w/ 35 levels "BW-0018\\10-24-23",..: 1 1 2 2 3 3 4 4 5 5 ...
## $ task_pose_hv : Factor w/ 2 levels "gait_vertical_left",..: 1 2 1 2 1 2 1 2 1 2 ...
## $ frames_per_second_pose_hv : int 30 30 30 30 30 30 30 30 30 30 ...
## $ total_video_duration_sec_pose_hv : num 26.8 24.4 36 38.5 36.1 ...
## $ delta_pix_h_rel_median_pose_hv : num 0.22 0.31 0.43 0.39 0.3 0.39 0.03 0.08 0.22 0.24 ...
## $ walking_segmets_n_pose_hv : num 1 1 4 5 3 2 NA NA 5 5 ...
## $ walking_segments_duration_mean_pose_hv : num 3.17 2.4 4.42 4.78 4.19 5.07 NA NA 4.47 3.77 ...
## $ walking_segments_duration_median_pose_hv : num 3.17 2.4 4.33 4.63 3.83 5.07 NA NA 4.3 3.7 ...
## $ stride_time_mean_sec_pose_hv : num 1.008 NA 1.027 1.07 0.998 ...
## $ stride_time_median_sec_pose_hv : num 1.05 NA 1.033 1.033 0.983 ...
## $ stride_time_std_pose_hv : num 0.171 NA 0.066 0.098 0.087 0.076 NA NA 0.229 0.078 ...
## $ stride_time_cv_pose_hv : num 16.94 NA 6.46 9.11 8.74 ...
## $ stride_time_max_pose_hv : num 1.17 NA 1.17 1.3 1.1 ...
## $ stride_time_min_pose_hv : num 0.767 NA 0.833 0.933 0.867 ...
## $ mean_cadence_step_per_min_pose_hv : num 133 75 113 106 111 ...
## $ stride_width_mean_cm_pose_hv : num 12.81 8.21 10.59 10.62 9.89 ...
## $ stride_width_median_cm_pose_hv : num 13.34 8.21 10.69 10.86 9.13 ...
## $ stride_width_std_pose_hv : num 3.54 10.46 3.52 3.6 3.19 ...
## $ stride_width_cv_pose_hv : num 27.6 127.4 33.3 33.9 32.3 ...
## $ stride_width_max_pose_hv : num 17.4 15.6 19.6 17.9 14.8 ...
## $ stride_width_min_pose_hv : num 7.921 0.813 4.529 0.624 4.146 ...
## $ foot1_gait_cycle_time_mean_pose_hv : num 1.3 NA NA NA NA ...
## $ foot1_stance_time_mean_pose_hv : num 0.9 NA NA NA NA ...
## $ foot1_stance_per_mean_pose_hv : num 69.2 NA NA NA NA ...
## $ foot1_swing_time_mean_pose_hv : num 0.4 NA NA NA NA 0.433 NA NA 0.333 0.433 ...
## $ foot1_swing_per_mean_pose_hv : num 30.8 NA NA NA NA ...
## $ foot1_ini_double_support_time_mean_pose_hv : num 0.4 NA NA NA NA 0.433 NA NA 0.5 0.333 ...
## $ foot1_term_double_support_time_mean_pose_hv: num 0.2 NA NA NA NA 0.033 NA NA 0.033 0.433 ...
## $ foot1_tot_double_support_time_mean_pose_hv : num 0.6 NA NA NA NA 0.467 NA NA 0.533 0.767 ...
## $ foot1_double_support_per_mean_pose_hv : num 46.2 NA NA NA NA ...
## $ foot1_single_support_time_mean_pose_hv : num 0.3 NA NA NA NA 0.467 NA NA 0.6 0.333 ...
## $ foot1_single_support_per_mean_pose_hv : num 23.1 NA NA NA NA ...
## $ walk_segment_pose_hv : num 0 NA NA NA NA 1 NA NA 1 1 ...
## $ foot1_pose_hv : chr "right" "" "" "" ...
## $ id_video : Factor w/ 31 levels "BW-0018","BW-0023",..: 1 1 2 2 2 2 3 3 4 4 ...
## $ visit_date_video : chr "2023-10-24" "2023-10-24" "2023-05-05" "2023-05-05" ...
## $ bw_id : Factor w/ 31 levels "BW-0018","BW-0023",..: 1 1 2 2 2 2 3 3 4 4 ...
## $ trialdate : chr "2023-10-24" "2023-10-24" "2023-04-26" "2023-04-26" ...
## $ visit_date : chr "2023-10-24" "2023-10-24" "2023-04-26" "2023-04-26" ...
## $ demoGait_dateDiff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DiseaseDuration : num 4 4 21 21 22 22 14 14 7 7 ...
## $ clean_sex : Factor w/ 3 levels "Female","Male",..: 2 2 1 1 1 1 1 1 1 1 ...
## $ clean_race : chr "White" "White" "Other" "Other" ...
## $ clean_ethnicity : chr "Not Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" "Not Hispanic Or Latino" ...
## $ demoEHR_Age : num 57 57 40 40 40 40 35 35 64 64 ...
## $ bingoEHR_DX_MS.DX : Factor w/ 4 levels "MS, Subtype Not Specified",..: 3 3 3 3 3 3 3 3 2 2 ...
## $ bingoEHR_EDSS_measure_value : Factor w/ 13 levels "0","1","1.5",..: 6 6 7 7 5 5 9 9 13 13 ...
## $ msfcEHR_T25FW.SPEED.AVG : num 3.6 3.6 3.4 3.4 4.4 4.4 4.4 4.4 6.6 6.6 ...
## $ FW_cadencestepsminmean : num 121 121 139 139 151 ...
## $ FW_singlesupportmean : num 32.5 32.5 38.9 38.9 39.9 ...
## $ FW_singlesupportratiolr : num 0.816 0.816 1.017 1.017 0.992 ...
## $ FW_stridetimeseccv : num 36.37 36.37 5.61 5.61 14.06 ...
## $ FW_stridetimesecmean : num 0.999 0.999 0.858 0.858 0.806 ...
## $ FW_stridewidthcmmean : num 5.55 5.55 6.85 6.85 9.31 ...
## $ FW_stridewidthcmsd : num 22.62 22.62 2.3 2.3 3.12 ...
## $ FW_totaldsupportmean : num 25.6 25.6 21.8 21.8 19.5 ...
## $ FW_totaldsupportratiolr : num 0.845 0.845 0.983 0.983 0.995 ...
## $ FW_velocitycmsecmean : num 123 123 190 190 205 ...
## $ PWS_cadencestepsminmean : num 116 116 112 112 127 ...
## $ PWS_singlesupportmean : num 34.8 34.8 35.8 35.8 36.9 ...
## $ PWS_singlesupportratiolr : num 0.952 0.952 1.038 1.038 1.02 ...
## $ PWS_stridetimeseccv : num 3.49 3.49 6.24 6.24 5.56 ...
## $ PWS_stridetimesecmean : num 1.028 1.028 1.065 1.065 0.945 ...
## $ PWS_stridewidthcmmean : num 8.19 8.19 8.33 8.33 8.69 ...
## $ PWS_stridewidthcmsd : num 3.72 3.72 2.38 2.38 1.95 ...
## $ PWS_totaldsupportmean : num 30.3 30.3 28.5 28.5 26.1 ...
## $ PWS_totaldsupportratiolr : num 1.01 1.01 0.98 0.98 1 ...
## $ PWS_velocitycmsecmean : num 137 137 116 116 140 ...
## $ redcap_event_name : Factor w/ 3 levels "Brainwalk: Baseline visit (Arm 1: Baseline visit)",..: 2 2 2 2 3 3 2 2 2 2 ...
## $ demographic_diagnosis : Factor w/ 1 level "MS": 1 1 1 1 1 1 1 1 1 1 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ demoEHR_DX_dateDiff : num 1617 1617 7969 7969 8149 ...
## $ demoEHR_EDSS_dateDiff : num 0 0 16 16 -182 -182 0 0 -111 -111 ...
## $ bingoEHR_Vitals_height.in. : num 67 67 66 66 66 66 66 66 67 67 ...
## $ demoEHR_Vitals_dateDiff : num -0.347 -0.347 15.553 15.553 -0.561 ...
## $ bingoEHR_first_MS.DX : chr "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" "RRMS (Relapsing-remitting Multiple Sclerosis)" ...
## $ edss_severity_num : num 2 2 2 2 2 2 3 3 3 3 ...
## $ edss_severity_cat : chr "moderate" "moderate" "moderate" "moderate" ...
## $ t25fw_group_num : num 1 1 1 1 1 1 1 1 2 2 ...
## $ t25fw_group_cat : chr "under_6" "under_6" "under_6" "under_6" ...
## $ race_ethnicity_clean : Factor w/ 5 levels "Asian","Black Or African American",..: 5 5 4 4 4 4 5 5 5 5 ...
## $ ms_dx_condensed : Factor w/ 3 levels "MS, Subtype Not Specified",..: 3 3 3 3 3 3 3 3 2 2 ...
## $ bw_hv_date_diff_days : int 0 0 9 9 0 0 -1 -1 -1 -1 ...# assign levels to categorical variables
table(home_df$race_ethnicity_clean)##
## Asian Black Or African American Hispanic or Latino
## 4 2 2
## Other/Unknown/Declined White Not Hispanic
## 9 48home_df$race_ethnicity_clean <- factor(home_df$race_ethnicity_clean,
levels = c('White Not Hispanic',
'Asian',
'Black Or African American',
'Hispanic or Latino',
'Other/Unknown/Declined'),
ordered = FALSE)
print(levels(home_df$race_ethnicity_clean))## [1] "White Not Hispanic" "Asian"
## [3] "Black Or African American" "Hispanic or Latino"
## [5] "Other/Unknown/Declined"table(home_df$ms_dx_condensed)##
## MS, Subtype Not Specified Progressive MS RRMS
## 4 9 52home_df$ms_dx_condensed <- factor(home_df$ms_dx_condensed,
levels = c('RRMS',
'Progressive MS',
'MS, Subtype Not Specified'),
ordered = FALSE)
print(levels(zeno_fw_df$ms_dx_condensed))## [1] "RRMS" "Progressive MS"
## [3] "MS, Subtype Not Specified"table(zeno_fw_df$clean_sex)##
## Female Male Non-Binary
## 167 53 2zeno_fw_df$clean_sex <- factor(zeno_fw_df$clean_sex,
levels = c('Female',
'Male',
'Non-Binary'),
ordered = FALSE)
print(levels(home_df$clean_sex))## [1] "Female" "Male" "Non-Binary"table(home_df$edss_severity_cat)##
## mild moderate severe
## 23 20 22home_df$edss_severity_cat <- factor(home_df$edss_severity_cat,
levels = c('mild',
'moderate',
'severe'),
ordered = TRUE)
print(levels(home_df$edss_severity_cat))## [1] "mild" "moderate" "severe"Missing variables per column
colSums(is.na(home_df))## X
## 0
## video_id_date_name_pose_hv
## 0
## id_date_pose_hv
## 0
## task_pose_hv
## 0
## frames_per_second_pose_hv
## 0
## total_video_duration_sec_pose_hv
## 0
## delta_pix_h_rel_median_pose_hv
## 4
## walking_segmets_n_pose_hv
## 12
## walking_segments_duration_mean_pose_hv
## 12
## walking_segments_duration_median_pose_hv
## 12
## stride_time_mean_sec_pose_hv
## 13
## stride_time_median_sec_pose_hv
## 13
## stride_time_std_pose_hv
## 14
## stride_time_cv_pose_hv
## 14
## stride_time_max_pose_hv
## 13
## stride_time_min_pose_hv
## 13
## mean_cadence_step_per_min_pose_hv
## 12
## stride_width_mean_cm_pose_hv
## 12
## stride_width_median_cm_pose_hv
## 12
## stride_width_std_pose_hv
## 12
## stride_width_cv_pose_hv
## 12
## stride_width_max_pose_hv
## 12
## stride_width_min_pose_hv
## 12
## foot1_gait_cycle_time_mean_pose_hv
## 34
## foot1_stance_time_mean_pose_hv
## 34
## foot1_stance_per_mean_pose_hv
## 34
## foot1_swing_time_mean_pose_hv
## 34
## foot1_swing_per_mean_pose_hv
## 34
## foot1_ini_double_support_time_mean_pose_hv
## 34
## foot1_term_double_support_time_mean_pose_hv
## 34
## foot1_tot_double_support_time_mean_pose_hv
## 34
## foot1_double_support_per_mean_pose_hv
## 34
## foot1_single_support_time_mean_pose_hv
## 34
## foot1_single_support_per_mean_pose_hv
## 34
## walk_segment_pose_hv
## 34
## foot1_pose_hv
## 0
## id_video
## 0
## visit_date_video
## 0
## bw_id
## 0
## trialdate
## 0
## visit_date
## 0
## demoGait_dateDiff
## 0
## demoEHR_DiseaseDuration
## 0
## clean_sex
## 0
## clean_race
## 0
## clean_ethnicity
## 0
## demoEHR_Age
## 0
## bingoEHR_DX_MS.DX
## 0
## bingoEHR_EDSS_measure_value
## 0
## msfcEHR_T25FW.SPEED.AVG
## 0
## FW_cadencestepsminmean
## 0
## FW_singlesupportmean
## 0
## FW_singlesupportratiolr
## 0
## FW_stridetimeseccv
## 0
## FW_stridetimesecmean
## 0
## FW_stridewidthcmmean
## 0
## FW_stridewidthcmsd
## 0
## FW_totaldsupportmean
## 0
## FW_totaldsupportratiolr
## 0
## FW_velocitycmsecmean
## 0
## PWS_cadencestepsminmean
## 0
## PWS_singlesupportmean
## 0
## PWS_singlesupportratiolr
## 0
## PWS_stridetimeseccv
## 0
## PWS_stridetimesecmean
## 0
## PWS_stridewidthcmmean
## 0
## PWS_stridewidthcmsd
## 0
## PWS_totaldsupportmean
## 0
## PWS_totaldsupportratiolr
## 0
## PWS_velocitycmsecmean
## 0
## redcap_event_name
## 0
## demographic_diagnosis
## 0
## msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff
## 0
## msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff
## 10
## demoEHR_DX_dateDiff
## 0
## demoEHR_EDSS_dateDiff
## 0
## bingoEHR_Vitals_height.in.
## 4
## demoEHR_Vitals_dateDiff
## 0
## bingoEHR_first_MS.DX
## 0
## edss_severity_num
## 0
## edss_severity_cat
## 0
## t25fw_group_num
## 0
## t25fw_group_cat
## 0
## race_ethnicity_clean
## 0
## ms_dx_condensed
## 0
## bw_hv_date_diff_days
## 0Visit types
# of right turning videos and left turning videos
table(home_df$task_pose_hv)##
## gait_vertical_left gait_vertical_right
## 32 33# videos per timepoint - most people sent two videos per timepoint
# some sent first videos at year 2 or year 3 visit, not necessarily a true follow up
table(home_df$redcap_event_name)##
## Brainwalk: Baseline visit (Arm 1: Baseline visit)
## 38
## Year 2 Visit (Arm 1: Baseline visit)
## 23
## Year 3 Visit (Arm 1: Baseline visit)
## 4Date diff histograms
ggplot(data = home_df, aes(bw_hv_date_diff_days)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Functions
Univariate Mixed Effect Model - video, demographics, MS info
all_univariate_mixed_effect <- function(data, outcome_column){
# remove hv or zv pattern in column name for consistency
colnames(data) <- gsub("pose_zv", "pose", colnames(data))
colnames(data) <- gsub("pose_hv", "pose", colnames(data))
################### video metrics ################################
# log delta pixel proxy ---------------------------------------------
print('log_delta_pix_h_rel_median_pose')
p <- ggplot(data = data,
aes(x = log_delta_pix_h_rel_median_pose,
y = .data[[outcome_column]])) +
geom_point()
print(p)
formula1 <- as.formula(paste(outcome_column,
"~ log_delta_pix_h_rel_median_pose + (1 | bw_id)"))
print(formula1)
uni1 <- lmer(formula1, data = data)
print(summary(uni1))
# Residuals vs. fitted values
hist(resid(uni1), main = "Residuals log_delta_pix_h_rel_median_pose")
# Normality check
qqnorm(residuals(uni1), main = "Normal Q-Q plot: log_delta_pix_h_rel_median_pose")
# uni df
uni1_df <- tidy(uni1, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni1)$R2_conditional) %>%
mutate(R2_marginal = r2(uni1)$R2_marginal) %>%
mutate(Variable = 'Log Pixel Height Proxy')
# stride time --------------------------------------------------------------
print('stride_time_median_sec_pose')
p <- ggplot(data = data,
aes(x = stride_time_median_sec_pose,
y = .data[[outcome_column]])) +
geom_point()
print(p)
formula2 <- as.formula(paste(outcome_column,
"~ stride_time_median_sec_pose + (1 | bw_id)"))
print(formula2)
uni2 <- lmer(formula2, data = data)
print(summary(uni2))
# Residuals vs. fitted values
hist(resid(uni2), main = "Residuals stride_time_median_sec_pose")
# Normality check
qqnorm(residuals(uni1), main = "Normal Q-Q plot: stride_time_median_sec_pose")
# uni df
uni2_df <- tidy(uni2, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni2)$R2_conditional) %>%
mutate(R2_marginal = r2(uni2)$R2_marginal) %>%
mutate(Variable = 'Stride Time - Median')
# cadence -------------------------------------------------------------------
print('mean_cadence_step_per_min_pose')
p <- ggplot(data = data,
aes(x = mean_cadence_step_per_min_pose,
y = .data[[outcome_column]])) +
geom_point()
print(p)
formula3 <- as.formula(paste(outcome_column,
"~ mean_cadence_step_per_min_pose + (1 | bw_id)"))
print(formula3)
uni3 <- lmer(formula3, data = data)
print(summary(uni3))
# Residuals vs. fitted values
hist(resid(uni3), main = "Residuals mean_cadence_step_per_min_pose")
# Normality check
qqnorm(residuals(uni3), main = "Normal Q-Q plot: mean_cadence_step_per_min_pose")
# uni df
uni3_df <- tidy(uni3, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni3)$R2_conditional) %>%
mutate(R2_marginal = r2(uni3)$R2_marginal) %>%
mutate(Variable = 'Cadence - Mean')
# stride width ----------------------------------------------------------
print('stride_width_median_cm_pose')
p <- ggplot(data = data,
aes(x = stride_width_median_cm_pose,
y = .data[[outcome_column]])) +
geom_point()
print(p)
formula4 <- as.formula(paste(outcome_column,
"~ stride_width_median_cm_pose + (1 | bw_id)"))
print(formula4)
uni4 <- lmer(formula4, data = data)
print(summary(uni4))
# Residuals vs. fitted values
hist(resid(uni4), main = "Residuals stride_width_median_cm_pose")
# Normality check
qqnorm(residuals(uni4), main = "Normal Q-Q plot: stride_width_median_cm_pose")
# uni df
uni4_df <- tidy(uni4, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni4)$R2_conditional) %>%
mutate(R2_marginal = r2(uni4)$R2_marginal) %>%
mutate(Variable = 'Stride Width - Median')
#### demographics and MS info ##################
# Age ---------------------------------------------
print('demoEHR_Age')
p <- ggplot(data = data,
aes(x = demoEHR_Age,
y = .data[[outcome_column]])) +
geom_point()
print(p)
formula5 <- as.formula(paste(outcome_column,
"~ demoEHR_Age + (1 | bw_id)"))
print(formula5)
uni5 <- lmer(formula5, data = data)
print(summary(uni5))
# Residuals vs. fitted values
hist(resid(uni5), main = "Residuals demoEHR_Age")
# Normality check
qqnorm(residuals(uni5), main = "Normal Q-Q plot: demoEHR_Age")
# uni df
uni5_df <- tidy(uni5, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni5)$R2_conditional) %>%
mutate(R2_marginal = r2(uni5)$R2_marginal) %>%
mutate(Variable = 'Age')
# Disease Duration ---------------------------------------------
print('demoEHR_DiseaseDuration')
p <- ggplot(data = data,
aes(x = demoEHR_DiseaseDuration,
y = .data[[outcome_column]])) +
geom_point()
print(p)
formula6 <- as.formula(paste(outcome_column,
"~ demoEHR_DiseaseDuration + (1 | bw_id)"))
print(formula6)
uni6 <- lmer(formula6, data = data)
print(summary(uni6))
# Residuals vs. fitted values
hist(resid(uni6), main = "Residuals demoEHR_DiseaseDuration")
# Normality check
qqnorm(residuals(uni6), main = "Normal Q-Q plot: demoEHR_DiseaseDuration")
# uni df
uni6_df <- tidy(uni6, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni6)$R2_conditional) %>%
mutate(R2_marginal = r2(uni6)$R2_marginal) %>%
mutate(Variable = 'Disease Duration')
# MS DX ---------------------------------------------
print('ms_dx_condensed')
formula7 <- as.formula(paste(outcome_column,
"~ ms_dx_condensed + (1 | bw_id)"))
print(formula7)
uni7 <- lmer(formula7, data = data)
print(summary(uni7))
# Residuals vs. fitted values
hist(resid(uni7), main = "Residuals ms_dx_condensed")
# Normality check
qqnorm(residuals(uni7), main = "Normal Q-Q plot: ms_dx_condensed")
# uni df
uni7_df <- tidy(uni7, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni7)$R2_conditional) %>%
mutate(R2_marginal = r2(uni7)$R2_marginal) %>%
mutate(Variable = 'MS DX')
# Race and Ethnicity ---------------------------------------------
print('race_ethnicity_clean')
formula8 <- as.formula(paste(outcome_column,
"~ race_ethnicity_clean + (1 | bw_id)"))
print(formula8)
uni8 <- lmer(formula8, data = data)
print(summary(uni8))
# Residuals vs. fitted values
hist(resid(uni8), main = "Residuals race_ethnicity_clean")
# Normality check
qqnorm(residuals(uni8), main = "Normal Q-Q plot: race_ethnicity_clean")
# uni df
uni8_df <- tidy(uni8, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni8)$R2_conditional) %>%
mutate(R2_marginal = r2(uni8)$R2_marginal) %>%
mutate(Variable = 'Race and Ethnicity')
# Sex ---------------------------------------------
print('clean_sex')
formula9 <- as.formula(paste(outcome_column,
"~ clean_sex + (1 | bw_id)"))
print(formula9)
uni9 <- lmer(formula9, data = data)
print(summary(uni9))
# Residuals vs. fitted values
hist(resid(uni9), main = "Residuals clean_sex")
# Normality check
qqnorm(residuals(uni9), main = "Normal Q-Q plot: clean_sex")
# uni df
uni9_df <- tidy(uni9, conf.int = TRUE) %>%
mutate(R2_conditional = r2(uni9)$R2_conditional) %>%
mutate(R2_marginal = r2(uni9)$R2_marginal) %>%
mutate(Variable = 'Sex')
# tidy and bind all together --------------------------------
tidy_uni_df <- bind_rows(uni1_df, uni2_df, uni3_df, uni4_df,
uni5_df, uni6_df, uni7_df, uni8_df,
uni9_df) %>%
mutate(Model = "Univariate Unadjusted")
return(tidy_uni_df)
}Round p-values for plotting
# Conditional rounding function
format_p_value <- function(p) {
if (p < 0.001) {
return(formatC(p, format = "e", digits = 1)) # Scientific notation, 1 decimal place
} else {
return(formatC(p, format = "f", digits = 2)) # Standard notation, 2 decimal places
}
}Plot unadjusted regression estimates
Should work for univariate or multivariate models - plot each variable as diff color Significance based transparency
univariate_regression_plot <- function(results, plot_title, x_adj) {
# Filter rows
results <- results %>%
filter(effect != 'ran_pars') %>%
filter(term != '(Intercept)')
results$Variable <- factor(results$Variable)
# Apply rounding function to p-value columns
results <- results %>% mutate(pval_text = paste0("p=", sapply(p.value, format_p_value))) # Format p-values
# Apply formatting function and create significance indicators
results <- results %>%
mutate(
pval_text = paste0("p=", sapply(p.value, format_p_value)),
sig = ifelse(p.value < 0.05, "Significant", "Non-Significant"), # Create significance group
alpha_level = ifelse(p.value < 0.05, 1, 0.3) # More transparency for non-sig points
)
# Determine x position for p-values (offset to the right of the max estimate)
x_max <- max(results$conf.high, na.rm = TRUE) # Get max confidence interval upper bound
results <- results %>% mutate(pval_x_pos = x_max + x_adj) # Place p-values slightly to the right of max x range
p <- ggplot(results, aes(x = estimate, y = term, color = Variable, alpha = alpha_level))+
geom_point(size = 3) + # dot = coefficient estimate
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0.2) + # Whiskers for confidence intervals
geom_text(aes(x = pval_x_pos, label = pval_text, fontface = ifelse(p.value < 0.05, "bold", "plain")),
hjust = 1, size = 3) + # Right-aligned p-values, bold if significant
scale_alpha_identity() + # Use manually set alpha levels
geom_vline(xintercept = 0, linetype = "dashed", color = "gray50") + # Reference line at zero
theme_minimal() +
labs(title = plot_title,
x = "Estimate (Effect Size)",
y = "Predictor") +
theme(legend.position = "right") # Hide legend to keep it clean
return(p)
}Plot Adjusted vs Unadjusted Coefficient estimates
adj_vs_unadj_plot <- function(results_df, plot_title, x_adj){
set.seed(42) # Set seed for reproducibility
# Filter rows
results_df <- results_df %>%
filter(effect != 'ran_pars') %>%
filter(term != '(Intercept)')
results_df <- results_df %>%
mutate(
pval_text = paste0("p=", sapply(p.value, format_p_value)), # Format p-values
sig = ifelse(is.na(p.value) | p.value >= 0.05, "Non-Significant", "Significant"), # Identify significance
alpha_level = ifelse(is.na(p.value) | p.value >= 0.05, 0.3, 1), # More transparency for non-sig points
jitter_y = as.numeric(factor(term)) + runif(n(), -0.2, 0.2) # Jitter y-axis values slightly
)
# Define colors for models
model_colors <- c("Unadjusted - Multivariate Demographics and MS Info" = "darkgreen",
"Unadjusted - Multivariate Video Metrics" = "darkblue",
"Adjusted - Multivariate Video Metrics" = "darkorange")
# Define shading by creating an index for each predictor group
results_df <- results_df %>%
arrange(term) %>%
mutate(group = as.integer(factor(term)) %% 2) # Alternating shading
# Determine x position for p-values (offset to the right of max estimate)
x_max <- max(results_df$conf.high, na.rm = TRUE) # Get max confidence interval upper bound
results_df <- results_df %>%
mutate(pval_x_pos = x_max + x_adj) # Place p-values slightly to the right of max x range
# Dot-and-whisker plot with enhancements
p <- ggplot(results_df, aes(x = estimate, y = term, color = Model, alpha = alpha_level)) +
# Add confidence intervals as horizontal whiskers
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0.2,
position = position_dodge(width = 0.5)) +
# Add dots for coefficient estimates
geom_point(position = position_dodge(width = 0.5), size = 3) +
# Add background shading for alternate rows
geom_rect(data = results_df, aes(ymin = as.numeric(factor(term)) - 0.5,
ymax = as.numeric(factor(term)) + 0.5,
xmin = -Inf, xmax = Inf,
fill = factor(group)),
inherit.aes = FALSE, alpha = 0.1) +
# Add vertical line at zero (null effect)
geom_vline(xintercept = 0, linetype = "dashed", color = "gray50") +
# Add jittered p-values at right side
geom_text(aes(x = pval_x_pos, y = jitter_y, label = pval_text,
fontface = ifelse(sig == "Significant", "bold", "plain")),
hjust = 1, size = 3) +
scale_color_manual(values = model_colors) + # Set custom colors for models
scale_fill_manual(values = c("white", "gray90")) + # Alternating shading
scale_alpha_identity() + # Use manually set alpha levels
theme_minimal() +
labs(title = plot_title,
x = "Estimate (Effect Size)",
y = "Predictor",
color = "Model") +
theme(legend.position = "right", # Move legend to the right
panel.grid.major = element_blank(), # Remove major grid lines for clarity
panel.grid.minor = element_blank())
return(p)
} T25FW Mixed Effect Model
Check all Date Diff <+- 90 days
# PWS
p1 <- ggplot(data = zeno_pws_df, aes(msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff)) +
geom_histogram()
p1## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# FW
p2 <- ggplot(data = zeno_fw_df, aes(msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff)) +
geom_histogram()
p2## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# home
p3 <- ggplot(data = home_df, aes(msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff)) +
geom_histogram()
p3## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot and transform T25FW
# preferred walking speed
ggplot(data = zeno_pws_df, mapping = aes(msfcEHR_T25FW.SPEED.AVG)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# log transform
zeno_pws_df$t25fw_log <- log(zeno_pws_df$msfcEHR_T25FW.SPEED.AVG)
ggplot(data = zeno_pws_df, mapping = aes(t25fw_log)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# inverse transformation
zeno_pws_df$t25fw_inverse <- 1/zeno_pws_df$msfcEHR_T25FW.SPEED.AVG
ggplot(data = zeno_pws_df, mapping = aes(t25fw_inverse)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# square root
zeno_pws_df$t25fw_sqrt <- scale(zeno_pws_df$msfcEHR_T25FW.SPEED.AVG)
ggplot(data = zeno_pws_df, mapping = aes(t25fw_sqrt)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Fast walking speed
ggplot(data = zeno_fw_df, mapping = aes(msfcEHR_T25FW.SPEED.AVG)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# log transform
zeno_fw_df$t25fw_log <- log(zeno_fw_df$msfcEHR_T25FW.SPEED.AVG)
ggplot(data = zeno_fw_df, mapping = aes(t25fw_log)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# inverse transformation
zeno_fw_df$t25fw_inverse <- 1/zeno_fw_df$msfcEHR_T25FW.SPEED.AVG
ggplot(data = zeno_fw_df, mapping = aes(t25fw_inverse)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# square root
zeno_fw_df$t25fw_sqrt <- scale(zeno_fw_df$msfcEHR_T25FW.SPEED.AVG)
ggplot(data = zeno_fw_df, mapping = aes(t25fw_sqrt)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Home Videos
ggplot(data = home_df, mapping = aes(msfcEHR_T25FW.SPEED.AVG)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# log transform
home_df$t25fw_log <- log(home_df$msfcEHR_T25FW.SPEED.AVG)
ggplot(data = home_df, mapping = aes(t25fw_log)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# inverse transformation
home_df$t25fw_inverse <- 1/home_df$msfcEHR_T25FW.SPEED.AVG
ggplot(data = home_df, mapping = aes(t25fw_inverse)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# square root
home_df$t25fw_sqrt <- scale(home_df$msfcEHR_T25FW.SPEED.AVG)
ggplot(data = home_df, mapping = aes(t25fw_sqrt)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Transform Velocity Proxy
log transform to improve linearity and replace inf values with NA
zeno_pws_df$log_delta_pix_h_rel_median_pose_zv <- log(zeno_pws_df$delta_pix_h_rel_median_pose_zv)
zeno_fw_df$log_delta_pix_h_rel_median_pose_zv <- log(zeno_fw_df$delta_pix_h_rel_median_pose_zv)
home_df$log_delta_pix_h_rel_median_pose_hv <- log(home_df$delta_pix_h_rel_median_pose_hv)
# convert inf to NaN
zeno_pws_df[] <- lapply(zeno_pws_df, function(x) {
if (is.numeric(x)) replace(x, is.infinite(x), NA) else x
})
zeno_fw_df[] <- lapply(zeno_fw_df, function(x) {
if (is.numeric(x)) replace(x, is.infinite(x), NA) else x
})
home_df[] <- lapply(home_df, function(x) {
if (is.numeric(x)) replace(x, is.infinite(x), NA) else x
})Preferred walking speed videos
Univariate Models
Outcome = Inverse Transform T25FW
uni_zv_pws_t25fw_inv <- all_univariate_mixed_effect(data = zeno_pws_df,
outcome_column = "t25fw_inverse")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 16 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ log_delta_pix_h_rel_median_pose + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: -600
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83368 -0.39416 -0.03095 0.34483 2.86758
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.002341 0.04839
## Residual 0.001201 0.03466
## Number of obs: 208, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.659e-01 1.111e-02 1.850e+02 23.940 < 2e-16
## log_delta_pix_h_rel_median_pose 4.113e-02 7.094e-03 1.571e+02 5.799 3.56e-08
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.909
## [1] "stride_time_median_sec_pose"## Warning: Removed 48 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: -526.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.66578 -0.34600 -0.03032 0.35330 2.62490
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0031003 0.05568
## Residual 0.0006519 0.02553
## Number of obs: 176, groups: bw_id, 128
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.25985 0.02404 84.00144 10.811 <2e-16 ***
## stride_time_median_sec_pose -0.04480 0.02077 76.73797 -2.157 0.0341 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.975
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 40 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ mean_cadence_step_per_min_pose + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: -535.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.77459 -0.32654 -0.02616 0.31830 2.74396
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0035632 0.05969
## Residual 0.0005904 0.02430
## Number of obs: 184, groups: bw_id, 132
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.824e-01 2.479e-02 1.022e+02 7.357 4.86e-11
## mean_cadence_step_per_min_pose 2.433e-04 2.341e-04 9.411e+01 1.039 0.301
##
## (Intercept) ***
## mean_cadence_step_per_min_pose
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.975
## [1] "stride_width_median_cm_pose"## Warning: Removed 40 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: -540.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.89660 -0.32519 -0.04361 0.32861 2.66680
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0034537 0.05877
## Residual 0.0006053 0.02460
## Number of obs: 184, groups: bw_id, 132
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.231562 0.014382 135.062508 16.100 <2e-16
## stride_width_median_cm_pose -0.001913 0.001059 109.991321 -1.807 0.0735
##
## (Intercept) ***
## stride_width_median_cm_pose .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.925
## [1] "demoEHR_Age"
## t25fw_inverse ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: -658
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39844 -0.34548 -0.01678 0.30502 2.52497
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0032803 0.05727
## Residual 0.0007357 0.02712
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.29148 0.02021 154.61031 14.422 < 2e-16 ***
## demoEHR_Age -0.00168 0.00039 155.26252 -4.308 2.91e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.969
## [1] "demoEHR_DiseaseDuration"
## t25fw_inverse ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: -644.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39967 -0.32293 -0.02153 0.27398 2.52378
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0036742 0.06061
## Residual 0.0007357 0.02712
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.178e-01 8.356e-03 1.567e+02 26.061 <2e-16 ***
## demoEHR_DiseaseDuration -1.095e-03 6.655e-04 1.579e+02 -1.645 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.778
## [1] "ms_dx_condensed"
## t25fw_inverse ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: -680.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.41935 -0.35078 -0.02125 0.34332 2.53863
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0028517 0.05340
## Residual 0.0007254 0.02693
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.222026 0.005262 152.754170
## ms_dx_condensedProgressive MS -0.078743 0.012093 155.073859
## ms_dx_condensedMS, Subtype Not Specified -0.010473 0.041482 151.165385
## t value Pr(>|t|)
## (Intercept) 42.195 < 2e-16 ***
## ms_dx_condensedProgressive MS -6.511 9.81e-10 ***
## ms_dx_condensedMS, Subtype Not Specified -0.252 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.435
## ms_d_MS,SNS -0.127 0.055
## [1] "race_ethnicity_clean"
## t25fw_inverse ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: -638.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.41228 -0.29878 -0.02029 0.27321 2.51217
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0036120 0.06010
## Residual 0.0007354 0.02712
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.116e-01 6.307e-03 1.492e+02
## race_ethnicity_cleanAsian -2.405e-02 2.044e-02 1.481e+02
## race_ethnicity_cleanBlack Or African American -5.186e-02 2.062e-02 1.534e+02
## race_ethnicity_cleanHispanic or Latino 8.896e-04 1.787e-02 1.503e+02
## race_ethnicity_cleanOther/Unknown/Declined 1.002e-02 1.969e-02 1.491e+02
## t value Pr(>|t|)
## (Intercept) 33.549 <2e-16 ***
## race_ethnicity_cleanAsian -1.176 0.2413
## race_ethnicity_cleanBlack Or African American -2.515 0.0129 *
## race_ethnicity_cleanHispanic or Latino 0.050 0.9604
## race_ethnicity_cleanOther/Unknown/Declined 0.509 0.6117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.309
## rc_thn_BOAA -0.306 0.094
## rc_thnc_HoL -0.353 0.109 0.108
## rc_th_O/U/D -0.320 0.099 0.098 0.113
## [1] "clean_sex"
## t25fw_inverse ~ clean_sex + (1 | bw_id)
## <environment: 0x00000000313784f8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: -644.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3582 -0.3165 -0.0239 0.2809 2.5705
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0037550 0.06128
## Residual 0.0007341 0.02709
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.040e-01 6.131e-03 1.517e+02 33.269 <2e-16 ***
## clean_sexMale 1.226e-02 1.231e-02 1.522e+02 0.996 0.321
## clean_sexNon-Binary 1.132e-02 6.449e-02 1.382e+02 0.175 0.861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.498
## cln_sxNn-Bn -0.095 0.047
# save adjusted r_squared
adj_r_squared_df <- uni_zv_pws_t25fw_inv[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.7 0.11
## 2 Stride Time - Median 0.83 0.01
## 3 Cadence - Mean 0.86 0
## 4 Stride Width - Median 0.85 0.01
## 5 Age 0.83 0.1
## 6 Disease Duration 0.84 0.02
## 7 MS DX 0.84 0.2
## 8 Race and Ethnicity 0.84 0.04
## 9 Sex 0.84 0.01write.csv(adj_r_squared_df, file.path(output_dir, 't25fw_inverse_univariate_pws_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_zv_pws_t25fw_inv,
plot_title = "PWS Univariate - lmer(Inverse T25FW ~ Predictor + (1 | bw_id))",
x_adj = 0.1)
p 
ggsave(file.path(output_dir, 't25fw_inverse_univariate_pws_estimates.png'),
bg = "white", width= 10, height=10)Drop rows with any missing video metrics
Not all metrics could be calculated from each video metric
Dropped videos missing any video metric so models could be compared
zeno_pws_clean_df <- zeno_pws_df %>%
drop_na(c("stride_time_median_sec_pose_zv",
"log_delta_pix_h_rel_median_pose_zv",
"mean_cadence_step_per_min_pose_zv",
"stride_width_median_cm_pose_zv"))
colSums(is.na(zeno_pws_clean_df))## X
## 0
## video_id_date_name_pose_zv
## 0
## id_date_pose_zv
## 0
## task_pose_zv
## 0
## frames_per_second_pose_zv
## 0
## total_video_duration_sec_pose_zv
## 0
## delta_pix_h_rel_median_pose_zv
## 0
## walking_segmets_n_pose_zv
## 0
## walking_segments_duration_mean_pose_zv
## 0
## walking_segments_duration_median_pose_zv
## 0
## stride_time_mean_sec_pose_zv
## 0
## stride_time_median_sec_pose_zv
## 0
## stride_time_std_pose_zv
## 5
## stride_time_cv_pose_zv
## 5
## stride_time_max_pose_zv
## 0
## stride_time_min_pose_zv
## 0
## mean_cadence_step_per_min_pose_zv
## 0
## stride_width_mean_cm_pose_zv
## 0
## stride_width_median_cm_pose_zv
## 0
## stride_width_std_pose_zv
## 1
## stride_width_cv_pose_zv
## 1
## stride_width_max_pose_zv
## 0
## stride_width_min_pose_zv
## 0
## foot1_gait_cycle_time_mean_pose_zv
## 109
## foot1_stance_time_mean_pose_zv
## 109
## foot1_stance_per_mean_pose_zv
## 109
## foot1_swing_time_mean_pose_zv
## 109
## foot1_swing_per_mean_pose_zv
## 109
## foot1_ini_double_support_time_mean_pose_zv
## 109
## foot1_term_double_support_time_mean_pose_zv
## 109
## foot1_tot_double_support_time_mean_pose_zv
## 109
## foot1_double_support_per_mean_pose_zv
## 109
## foot1_single_support_time_mean_pose_zv
## 109
## foot1_single_support_per_mean_pose_zv
## 109
## walk_segment_pose_zv
## 109
## foot1_pose_zv
## 0
## id_video
## 0
## visit_date_video
## 0
## bw_id
## 0
## trialdate
## 0
## visit_date
## 0
## demoGait_dateDiff
## 0
## demoEHR_DiseaseDuration
## 0
## clean_sex
## 0
## clean_race
## 0
## clean_ethnicity
## 0
## demoEHR_Age
## 0
## bingoEHR_DX_MS.DX
## 0
## bingoEHR_EDSS_measure_value
## 0
## msfcEHR_T25FW.SPEED.AVG
## 0
## FW_cadencestepsminmean
## 0
## FW_singlesupportmean
## 0
## FW_singlesupportratiolr
## 0
## FW_stridetimeseccv
## 0
## FW_stridetimesecmean
## 0
## FW_stridewidthcmmean
## 0
## FW_stridewidthcmsd
## 0
## FW_totaldsupportmean
## 0
## FW_totaldsupportratiolr
## 0
## FW_velocitycmsecmean
## 0
## PWS_cadencestepsminmean
## 0
## PWS_singlesupportmean
## 0
## PWS_singlesupportratiolr
## 0
## PWS_stridetimeseccv
## 0
## PWS_stridetimesecmean
## 0
## PWS_stridewidthcmmean
## 0
## PWS_stridewidthcmsd
## 0
## PWS_totaldsupportmean
## 0
## PWS_totaldsupportratiolr
## 0
## PWS_velocitycmsecmean
## 0
## redcap_event_name
## 0
## demographic_diagnosis
## 0
## msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff
## 0
## msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff
## 19
## demoEHR_DX_dateDiff
## 0
## demoEHR_EDSS_dateDiff
## 0
## bingoEHR_Vitals_height.in.
## 11
## demoEHR_Vitals_dateDiff
## 0
## bingoEHR_first_MS.DX
## 0
## edss_severity_num
## 0
## edss_severity_cat
## 0
## t25fw_group_num
## 0
## t25fw_group_cat
## 0
## race_ethnicity_clean
## 0
## ms_dx_condensed
## 0
## t25fw_log
## 0
## t25fw_inverse
## 0
##
## 0
## log_delta_pix_h_rel_median_pose_zv
## 0Multivariate Demographics and MS Info
t25fw_pws_multi_dem <- lmer(t25fw_inverse ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_pws_clean_df)
summary(t25fw_pws_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_pws_clean_df
##
## REML criterion at convergence: -435.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.59107 -0.35899 -0.00299 0.32896 2.56814
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0024740 0.04974
## Residual 0.0006878 0.02623
## Number of obs: 162, groups: bw_id, 127
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.937e-01 2.458e-02 1.165e+02
## demoEHR_Age -1.347e-03 5.060e-04 1.188e+02
## demoEHR_DiseaseDuration 3.413e-05 7.191e-04 1.181e+02
## ms_dx_condensedProgressive MS -5.618e-02 1.438e-02 1.181e+02
## ms_dx_condensedMS, Subtype Not Specified 7.201e-03 4.113e-02 1.231e+02
## race_ethnicity_cleanAsian -3.828e-02 2.119e-02 1.180e+02
## race_ethnicity_cleanBlack Or African American -7.868e-02 1.943e-02 1.138e+02
## race_ethnicity_cleanHispanic or Latino -1.357e-02 1.728e-02 1.160e+02
## race_ethnicity_cleanOther/Unknown/Declined -2.256e-02 2.180e-02 1.187e+02
## clean_sexMale 7.427e-03 1.146e-02 1.176e+02
## clean_sexNon-Binary -9.488e-03 5.706e-02 1.234e+02
## t value Pr(>|t|)
## (Intercept) 11.947 < 2e-16 ***
## demoEHR_Age -2.662 0.008854 **
## demoEHR_DiseaseDuration 0.047 0.962222
## ms_dx_condensedProgressive MS -3.906 0.000157 ***
## ms_dx_condensedMS, Subtype Not Specified 0.175 0.861319
## race_ethnicity_cleanAsian -1.807 0.073326 .
## race_ethnicity_cleanBlack Or African American -4.050 9.38e-05 ***
## race_ethnicity_cleanHispanic or Latino -0.785 0.433864
## race_ethnicity_cleanOther/Unknown/Declined -1.035 0.302815
## clean_sexMale 0.648 0.518264
## clean_sexNon-Binary -0.166 0.868205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__OAA r__HoL r__O/U
## demoEHR_Age -0.920
## dmEHR_DssDr 0.183 -0.454
## ms_dx_cnPMS 0.119 -0.180 -0.120
## ms_d_MS,SNS 0.130 -0.142 -0.047 0.115
## rc_thncty_A -0.285 0.217 -0.033 -0.012 0.008
## rc_thn_BOAA -0.164 0.077 -0.003 0.038 0.031 0.107
## rc_thnc_HoL -0.315 0.238 -0.046 0.031 0.023 0.167 0.129
## rc_th_O/U/D -0.353 0.254 -0.033 0.053 -0.009 0.136 0.117 0.154
## clean_sexMl -0.153 0.032 0.002 0.004 -0.075 -0.053 0.027 -0.126 0.144
## cln_sxNn-Bn -0.056 -0.012 0.107 0.022 0.002 0.034 0.039 0.039 0.049
## cln_sM
## demoEHR_Age
## dmEHR_DssDr
## ms_dx_cnPMS
## ms_d_MS,SNS
## rc_thncty_A
## rc_thn_BOAA
## rc_thnc_HoL
## rc_th_O/U/D
## clean_sexMl
## cln_sxNn-Bn 0.053# Residuals vs. fitted values
hist(resid(t25fw_pws_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(t25fw_pws_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
t25fw_pws_multi_dem_df <- tidy(t25fw_pws_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_pws_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_pws_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
t25fw_pws_multi_dem_df## # A tibble: 13 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Int~ 2.94e-1 0.0246 11.9 116. 5.79e-22 0.245
## 2 fixed <NA> demo~ -1.35e-3 0.000506 -2.66 119. 8.85e- 3 -0.00235
## 3 fixed <NA> demo~ 3.41e-5 0.000719 0.0475 118. 9.62e- 1 -0.00139
## 4 fixed <NA> ms_d~ -5.62e-2 0.0144 -3.91 118. 1.57e- 4 -0.0847
## 5 fixed <NA> ms_d~ 7.20e-3 0.0411 0.175 123. 8.61e- 1 -0.0742
## 6 fixed <NA> race~ -3.83e-2 0.0212 -1.81 118. 7.33e- 2 -0.0802
## 7 fixed <NA> race~ -7.87e-2 0.0194 -4.05 114. 9.38e- 5 -0.117
## 8 fixed <NA> race~ -1.36e-2 0.0173 -0.785 116. 4.34e- 1 -0.0478
## 9 fixed <NA> race~ -2.26e-2 0.0218 -1.03 119. 3.03e- 1 -0.0657
## 10 fixed <NA> clea~ 7.43e-3 0.0115 0.648 118. 5.18e- 1 -0.0153
## 11 fixed <NA> clea~ -9.49e-3 0.0571 -0.166 123. 8.68e- 1 -0.122
## 12 ran_pars bw_id sd__~ 4.97e-2 NA NA NA NA NA
## 13 ran_pars Residual sd__~ 2.62e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
t25fw_pws_multi_vid_unadj <- lmer(t25fw_inverse ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
(1|bw_id),
data = zeno_pws_clean_df)
summary(t25fw_pws_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## (1 | bw_id)
## Data: zeno_pws_clean_df
##
## REML criterion at convergence: -449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8105 -0.3820 -0.0337 0.3811 2.8204
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.002209 0.04700
## Residual 0.001025 0.03201
## Number of obs: 162, groups: bw_id, 127
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.695e-01 7.623e-02 9.810e+01 4.848
## stride_time_median_sec_pose_zv -7.166e-02 3.423e-02 8.938e+01 -2.093
## log_delta_pix_h_rel_median_pose_zv 2.007e-02 8.571e-03 1.025e+02 2.341
## mean_cadence_step_per_min_pose_zv 9.103e-06 4.087e-04 9.721e+01 0.022
## stride_width_median_cm_pose_zv -4.111e-03 1.355e-03 1.530e+02 -3.033
## Pr(>|t|)
## (Intercept) 4.68e-06 ***
## stride_time_median_sec_pose_zv 0.03915 *
## log_delta_pix_h_rel_median_pose_zv 0.02116 *
## mean_cadence_step_per_min_pose_zv 0.98228
## stride_width_median_cm_pose_zv 0.00284 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.800
## lg_dl______ 0.116 0.202
## mn_cdn_____ -0.891 0.550 -0.136
## strd_wd____ -0.326 0.083 0.084 0.131# Residuals vs. fitted values
hist(resid(t25fw_pws_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(t25fw_pws_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
t25fw_pws_multi_vid_unadj_df <- tidy(t25fw_pws_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_pws_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_pws_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
t25fw_pws_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inter~ 3.70e-1 0.0762 4.85 98.1 4.68e-6 2.18e-1
## 2 fixed <NA> stride~ -7.17e-2 0.0342 -2.09 89.4 3.92e-2 -1.40e-1
## 3 fixed <NA> log_de~ 2.01e-2 0.00857 2.34 103. 2.12e-2 3.07e-3
## 4 fixed <NA> mean_c~ 9.10e-6 0.000409 0.0223 97.2 9.82e-1 -8.02e-4
## 5 fixed <NA> stride~ -4.11e-3 0.00136 -3.03 153. 2.84e-3 -6.79e-3
## 6 ran_pars bw_id sd__(I~ 4.70e-2 NA NA NA NA NA
## 7 ran_pars Residual sd__Ob~ 3.20e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
t25fw_pws_multi_vid_adj <- lmer(t25fw_inverse ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_pws_clean_df)
summary(t25fw_pws_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_pws_clean_df
##
## REML criterion at convergence: -410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02262 -0.42154 -0.01768 0.38626 2.87797
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0016891 0.04110
## Residual 0.0009969 0.03157
## Number of obs: 162, groups: bw_id, 127
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 3.719e-01 7.889e-02 1.040e+02
## stride_time_median_sec_pose_zv -4.763e-02 3.365e-02 8.893e+01
## log_delta_pix_h_rel_median_pose_zv 1.681e-02 8.419e-03 9.883e+01
## mean_cadence_step_per_min_pose_zv 2.970e-04 3.993e-04 9.346e+01
## stride_width_median_cm_pose_zv -2.346e-03 1.364e-03 1.458e+02
## demoEHR_Age -1.474e-03 4.631e-04 1.102e+02
## demoEHR_DiseaseDuration -6.427e-05 6.634e-04 1.065e+02
## ms_dx_condensedProgressive MS -3.057e-02 1.476e-02 1.187e+02
## ms_dx_condensedMS, Subtype Not Specified 6.442e-03 3.805e-02 1.217e+02
## race_ethnicity_cleanAsian -3.325e-02 1.965e-02 1.123e+02
## race_ethnicity_cleanBlack Or African American -7.274e-02 1.793e-02 1.040e+02
## race_ethnicity_cleanHispanic or Latino -1.697e-02 1.578e-02 1.057e+02
## race_ethnicity_cleanOther/Unknown/Declined -1.820e-02 2.017e-02 1.149e+02
## clean_sexMale 1.041e-02 1.072e-02 1.123e+02
## clean_sexNon-Binary -1.608e-02 5.271e-02 1.220e+02
## t value Pr(>|t|)
## (Intercept) 4.714 7.57e-06 ***
## stride_time_median_sec_pose_zv -1.416 0.1604
## log_delta_pix_h_rel_median_pose_zv 1.996 0.0487 *
## mean_cadence_step_per_min_pose_zv 0.744 0.4588
## stride_width_median_cm_pose_zv -1.720 0.0876 .
## demoEHR_Age -3.182 0.0019 **
## demoEHR_DiseaseDuration -0.097 0.9230
## ms_dx_condensedProgressive MS -2.071 0.0405 *
## ms_dx_condensedMS, Subtype Not Specified 0.169 0.8658
## race_ethnicity_cleanAsian -1.692 0.0934 .
## race_ethnicity_cleanBlack Or African American -4.058 9.61e-05 ***
## race_ethnicity_cleanHispanic or Latino -1.076 0.2846
## race_ethnicity_cleanOther/Unknown/Declined -0.902 0.3688
## clean_sexMale 0.971 0.3335
## clean_sexNon-Binary -0.305 0.7609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Residuals vs. fitted values
hist(resid(t25fw_pws_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(t25fw_pws_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
t25fw_pws_multi_vid_adj_df <- tidy(t25fw_pws_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_pws_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_pws_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")
t25fw_pws_multi_vid_adj_df## # A tibble: 17 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 3.72e-1 0.0789 4.71 104. 7.57e-6 2.15e-1
## 2 fixed <NA> strid~ -4.76e-2 0.0336 -1.42 88.9 1.60e-1 -1.14e-1
## 3 fixed <NA> log_d~ 1.68e-2 0.00842 2.00 98.8 4.87e-2 1.00e-4
## 4 fixed <NA> mean_~ 2.97e-4 0.000399 0.744 93.5 4.59e-1 -4.96e-4
## 5 fixed <NA> strid~ -2.35e-3 0.00136 -1.72 146. 8.76e-2 -5.04e-3
## 6 fixed <NA> demoE~ -1.47e-3 0.000463 -3.18 110. 1.90e-3 -2.39e-3
## 7 fixed <NA> demoE~ -6.43e-5 0.000663 -0.0969 107. 9.23e-1 -1.38e-3
## 8 fixed <NA> ms_dx~ -3.06e-2 0.0148 -2.07 119. 4.05e-2 -5.98e-2
## 9 fixed <NA> ms_dx~ 6.44e-3 0.0381 0.169 122. 8.66e-1 -6.89e-2
## 10 fixed <NA> race_~ -3.32e-2 0.0196 -1.69 112. 9.34e-2 -7.22e-2
## 11 fixed <NA> race_~ -7.27e-2 0.0179 -4.06 104. 9.61e-5 -1.08e-1
## 12 fixed <NA> race_~ -1.70e-2 0.0158 -1.08 106. 2.85e-1 -4.83e-2
## 13 fixed <NA> race_~ -1.82e-2 0.0202 -0.902 115. 3.69e-1 -5.82e-2
## 14 fixed <NA> clean~ 1.04e-2 0.0107 0.971 112. 3.34e-1 -1.08e-2
## 15 fixed <NA> clean~ -1.61e-2 0.0527 -0.305 122. 7.61e-1 -1.20e-1
## 16 ran_pars bw_id sd__(~ 4.11e-2 NA NA NA NA NA
## 17 ran_pars Residual sd__O~ 3.16e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>ANOVA - Compare Multivar Models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(t25fw_pws_multi_dem, t25fw_pws_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_pws_clean_df
## Models:
## t25fw_pws_multi_dem: t25fw_inverse ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## t25fw_pws_multi_vid_adj: t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## t25fw_pws_multi_dem 13 -491.71 -451.57 258.85 -517.71
## t25fw_pws_multi_vid_adj 17 -498.83 -446.34 266.42 -532.83 15.122 4
## Pr(>Chisq)
## t25fw_pws_multi_dem
## t25fw_pws_multi_vid_adj 0.004454 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Video only vs Video + Demographics
anova(t25fw_pws_multi_vid_unadj, t25fw_pws_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_pws_clean_df
## Models:
## t25fw_pws_multi_vid_unadj: t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + (1 | bw_id)
## t25fw_pws_multi_vid_adj: t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## t25fw_pws_multi_vid_unadj 7 -482.45 -460.84 248.22 -496.45
## t25fw_pws_multi_vid_adj 17 -498.83 -446.34 266.42 -532.83 36.381 10
## Pr(>Chisq)
## t25fw_pws_multi_vid_unadj
## t25fw_pws_multi_vid_adj 7.239e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
t25fw_pws_multivar_all <- bind_rows(t25fw_pws_multi_dem_df,
t25fw_pws_multi_vid_unadj_df,
t25fw_pws_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- t25fw_pws_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.84 0.26
## 2 Unadjusted - Multivariate Video Metrics 0.73 0.15
## 3 Adjusted - Multivariate Video Metrics 0.76 0.35write.csv(adj_r_squared_df, file.path(output_dir, 't25fw_inverse_adj_vs_unadj_pws_r2.csv'))Plot Unadjusted vs Adjusted Estimates
p <- adj_vs_unadj_plot(results_df = t25fw_pws_multivar_all,
plot_title = "PWS: Inverse T25FW Unadjusted vs Adjusted",
x_adj = 0.1)
p
ggsave(file.path(output_dir, 't25fw_inverse_adj_vs_unadj_pws_estimates.png'),
bg = "white", width= 12, height=10)Fast Walking Speed Videos
Univariate Models
Outcome = Inverse Transform T25FW
uni_zv_fw_t25fw_inv <- all_univariate_mixed_effect(data = zeno_fw_df,
outcome_column = "t25fw_inverse")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 4 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ log_delta_pix_h_rel_median_pose + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: -666.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1873 -0.4170 -0.0125 0.4484 3.1930
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.001521 0.03900
## Residual 0.001331 0.03648
## Number of obs: 218, groups: bw_id, 152
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.687e-01 7.860e-03 2.160e+02 34.192 <2e-16
## log_delta_pix_h_rel_median_pose 5.702e-02 6.285e-03 1.926e+02 9.071 <2e-16
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.855
## [1] "stride_time_median_sec_pose"## Warning: Removed 53 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: -527.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54287 -0.32608 -0.03509 0.34992 2.73440
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0022629 0.04757
## Residual 0.0007267 0.02696
## Number of obs: 169, groups: bw_id, 126
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.38706 0.02333 166.99441 16.59 < 2e-16 ***
## stride_time_median_sec_pose -0.18767 0.02391 166.62762 -7.85 4.81e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.979
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 46 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ mean_cadence_step_per_min_pose + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: -502.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.10312 -0.38242 -0.04459 0.30139 2.91658
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.002570 0.05070
## Residual 0.001031 0.03211
## Number of obs: 176, groups: bw_id, 129
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.623e-02 2.308e-02 1.545e+02 4.170 5.06e-05
## mean_cadence_step_per_min_pose 9.273e-04 1.891e-04 1.500e+02 4.903 2.42e-06
##
## (Intercept) ***
## mean_cadence_step_per_min_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.975
## [1] "stride_width_median_cm_pose"## Warning: Removed 47 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: -492.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7530 -0.3171 -0.0240 0.3067 2.3262
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0036487 0.06040
## Residual 0.0007272 0.02697
## Number of obs: 175, groups: bw_id, 128
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.235769 0.015987 108.428338 14.747 <2e-16
## stride_width_median_cm_pose -0.002414 0.001233 88.309028 -1.957 0.0535
##
## (Intercept) ***
## stride_width_median_cm_pose .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.933
## [1] "demoEHR_Age"
## t25fw_inverse ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: -647.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.33199 -0.34903 -0.01902 0.32366 2.46221
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0032377 0.05690
## Residual 0.0007759 0.02785
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.921e-01 2.019e-02 1.543e+02 14.465 < 2e-16 ***
## demoEHR_Age -1.692e-03 3.896e-04 1.551e+02 -4.342 2.54e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.969
## [1] "demoEHR_DiseaseDuration"
## t25fw_inverse ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: -633.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.33333 -0.32325 -0.02284 0.28068 2.46155
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0036396 0.06033
## Residual 0.0007756 0.02785
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.177e-01 8.365e-03 1.561e+02 26.028 <2e-16 ***
## demoEHR_DiseaseDuration -1.095e-03 6.682e-04 1.580e+02 -1.638 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.779
## [1] "ms_dx_condensed"
## t25fw_inverse ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: -669.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.35541 -0.35579 -0.01338 0.33543 2.49972
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0028261 0.05316
## Residual 0.0007631 0.02762
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.221930 0.005264 152.420568
## ms_dx_condensedProgressive MS -0.078609 0.012120 155.822998
## ms_dx_condensedMS, Subtype Not Specified -0.007644 0.042688 168.148599
## t value Pr(>|t|)
## (Intercept) 42.161 < 2e-16 ***
## ms_dx_condensedProgressive MS -6.486 1.11e-09 ***
## ms_dx_condensedMS, Subtype Not Specified -0.179 0.858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.434
## ms_d_MS,SNS -0.123 0.054
## [1] "race_ethnicity_clean"
## t25fw_inverse ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: -627.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3451 -0.3121 -0.0178 0.2820 2.4481
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0035736 0.05978
## Residual 0.0007762 0.02786
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.211444 0.006304 148.737325
## race_ethnicity_cleanAsian -0.023811 0.020423 147.275407
## race_ethnicity_cleanBlack Or African American -0.051771 0.020613 152.955864
## race_ethnicity_cleanHispanic or Latino 0.001897 0.017904 151.274717
## race_ethnicity_cleanOther/Unknown/Declined 0.010172 0.019672 148.312174
## t value Pr(>|t|)
## (Intercept) 33.542 <2e-16 ***
## race_ethnicity_cleanAsian -1.166 0.2455
## race_ethnicity_cleanBlack Or African American -2.512 0.0131 *
## race_ethnicity_cleanHispanic or Latino 0.106 0.9158
## race_ethnicity_cleanOther/Unknown/Declined 0.517 0.6059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.309
## rc_thn_BOAA -0.306 0.094
## rc_thnc_HoL -0.352 0.109 0.108
## rc_th_O/U/D -0.320 0.099 0.098 0.113
## [1] "clean_sex"
## t25fw_inverse ~ clean_sex + (1 | bw_id)
## <environment: 0x00000000307ae9f0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: -633.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29045 -0.32315 -0.02408 0.28858 2.50979
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0037188 0.06098
## Residual 0.0007739 0.02782
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.039e-01 6.129e-03 1.513e+02 33.267 <2e-16 ***
## clean_sexMale 1.250e-02 1.232e-02 1.523e+02 1.015 0.312
## clean_sexNon-Binary 1.139e-02 6.437e-02 1.369e+02 0.177 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.498
## cln_sxNn-Bn -0.095 0.047
# save adjusted r_squared
adj_r_squared_df <- uni_zv_fw_t25fw_inv[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.65 0.25
## 2 Stride Time - Median 0.82 0.25
## 3 Cadence - Mean 0.74 0.1
## 4 Stride Width - Median 0.84 0.01
## 5 Age 0.83 0.1
## 6 Disease Duration 0.83 0.02
## 7 MS DX 0.83 0.2
## 8 Race and Ethnicity 0.83 0.05
## 9 Sex 0.83 0.01write.csv(adj_r_squared_df, file.path(output_dir, 't25fw_inverse_univariate_fw_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_zv_fw_t25fw_inv,
plot_title = "FW Univariate - lmer(Inverse T25FW ~ Predictor + (1 | bw_id))",
x_adj = 0.1)
p 
ggsave(file.path(output_dir, 't25fw_inverse_univariate_fw_estimates.png'),
bg = "white", width= 10, height=10)Drop rows with any missing video metrics
Not all metrics could be calculated from each video metric
Dropped videos missing any video metric so models could be compared
zeno_fw_clean_df <- zeno_fw_df %>%
drop_na(c("stride_time_median_sec_pose_zv",
"log_delta_pix_h_rel_median_pose_zv",
"mean_cadence_step_per_min_pose_zv",
"stride_width_median_cm_pose_zv"))
colSums(is.na(zeno_fw_clean_df))## X
## 0
## video_id_date_name_pose_zv
## 0
## id_date_pose_zv
## 0
## task_pose_zv
## 0
## frames_per_second_pose_zv
## 0
## total_video_duration_sec_pose_zv
## 0
## delta_pix_h_rel_median_pose_zv
## 0
## walking_segmets_n_pose_zv
## 0
## walking_segments_duration_mean_pose_zv
## 0
## walking_segments_duration_median_pose_zv
## 0
## stride_time_mean_sec_pose_zv
## 0
## stride_time_median_sec_pose_zv
## 0
## stride_time_std_pose_zv
## 1
## stride_time_cv_pose_zv
## 1
## stride_time_max_pose_zv
## 0
## stride_time_min_pose_zv
## 0
## mean_cadence_step_per_min_pose_zv
## 0
## stride_width_mean_cm_pose_zv
## 0
## stride_width_median_cm_pose_zv
## 0
## stride_width_std_pose_zv
## 1
## stride_width_cv_pose_zv
## 1
## stride_width_max_pose_zv
## 0
## stride_width_min_pose_zv
## 0
## foot1_gait_cycle_time_mean_pose_zv
## 134
## foot1_stance_time_mean_pose_zv
## 134
## foot1_stance_per_mean_pose_zv
## 134
## foot1_swing_time_mean_pose_zv
## 134
## foot1_swing_per_mean_pose_zv
## 134
## foot1_ini_double_support_time_mean_pose_zv
## 134
## foot1_term_double_support_time_mean_pose_zv
## 134
## foot1_tot_double_support_time_mean_pose_zv
## 134
## foot1_double_support_per_mean_pose_zv
## 134
## foot1_single_support_time_mean_pose_zv
## 134
## foot1_single_support_per_mean_pose_zv
## 134
## walk_segment_pose_zv
## 134
## foot1_pose_zv
## 0
## id_video
## 0
## visit_date_video
## 0
## bw_id
## 0
## trialdate
## 0
## visit_date
## 0
## demoGait_dateDiff
## 0
## demoEHR_DiseaseDuration
## 0
## clean_sex
## 0
## clean_race
## 0
## clean_ethnicity
## 0
## demoEHR_Age
## 0
## bingoEHR_DX_MS.DX
## 0
## bingoEHR_EDSS_measure_value
## 0
## msfcEHR_T25FW.SPEED.AVG
## 0
## FW_cadencestepsminmean
## 0
## FW_singlesupportmean
## 0
## FW_singlesupportratiolr
## 0
## FW_stridetimeseccv
## 0
## FW_stridetimesecmean
## 0
## FW_stridewidthcmmean
## 0
## FW_stridewidthcmsd
## 0
## FW_totaldsupportmean
## 0
## FW_totaldsupportratiolr
## 0
## FW_velocitycmsecmean
## 0
## PWS_cadencestepsminmean
## 0
## PWS_singlesupportmean
## 0
## PWS_singlesupportratiolr
## 0
## PWS_stridetimeseccv
## 0
## PWS_stridetimesecmean
## 0
## PWS_stridewidthcmmean
## 0
## PWS_stridewidthcmsd
## 0
## PWS_totaldsupportmean
## 0
## PWS_totaldsupportratiolr
## 0
## PWS_velocitycmsecmean
## 0
## redcap_event_name
## 0
## demographic_diagnosis
## 0
## msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff
## 0
## msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff
## 23
## demoEHR_DX_dateDiff
## 0
## demoEHR_EDSS_dateDiff
## 0
## bingoEHR_Vitals_height.in.
## 10
## demoEHR_Vitals_dateDiff
## 0
## bingoEHR_first_MS.DX
## 0
## edss_severity_num
## 0
## edss_severity_cat
## 0
## t25fw_group_num
## 0
## t25fw_group_cat
## 0
## race_ethnicity_clean
## 0
## ms_dx_condensed
## 0
## t25fw_log
## 0
## t25fw_inverse
## 0
##
## 0
## log_delta_pix_h_rel_median_pose_zv
## 0Multivariate Demographics and MS Info
t25fw_fw_multi_dem <- lmer(t25fw_inverse ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_fw_clean_df)
summary(t25fw_fw_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_fw_clean_df
##
## REML criterion at convergence: -453.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5748 -0.3574 -0.0063 0.3115 2.5189
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0025301 0.05030
## Residual 0.0007046 0.02654
## Number of obs: 167, groups: bw_id, 125
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 3.002e-01 2.477e-02 1.186e+02
## demoEHR_Age -1.497e-03 4.905e-04 1.208e+02
## demoEHR_DiseaseDuration 1.761e-04 7.341e-04 1.185e+02
## ms_dx_condensedProgressive MS -6.413e-02 1.452e-02 1.185e+02
## ms_dx_condensedMS, Subtype Not Specified -3.678e-02 5.931e-02 1.239e+02
## race_ethnicity_cleanAsian -4.925e-02 1.952e-02 1.164e+02
## race_ethnicity_cleanBlack Or African American -7.915e-02 1.973e-02 1.151e+02
## race_ethnicity_cleanHispanic or Latino -1.727e-02 1.774e-02 1.156e+02
## race_ethnicity_cleanOther/Unknown/Declined -7.756e-03 2.011e-02 1.179e+02
## clean_sexMale 1.051e-02 1.192e-02 1.156e+02
## clean_sexNon-Binary -1.608e-02 5.469e-02 9.961e+01
## t value Pr(>|t|)
## (Intercept) 12.120 < 2e-16 ***
## demoEHR_Age -3.052 0.002794 **
## demoEHR_DiseaseDuration 0.240 0.810858
## ms_dx_condensedProgressive MS -4.417 2.22e-05 ***
## ms_dx_condensedMS, Subtype Not Specified -0.620 0.536320
## race_ethnicity_cleanAsian -2.523 0.012984 *
## race_ethnicity_cleanBlack Or African American -4.013 0.000107 ***
## race_ethnicity_cleanHispanic or Latino -0.974 0.332201
## race_ethnicity_cleanOther/Unknown/Declined -0.386 0.700445
## clean_sexMale 0.882 0.379808
## clean_sexNon-Binary -0.294 0.769392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__OAA r__HoL r__O/U
## demoEHR_Age -0.907
## dmEHR_DssDr 0.091 -0.401
## ms_dx_cnPMS 0.150 -0.213 -0.102
## ms_d_MS,SNS 0.097 -0.053 -0.169 0.095
## rc_thncty_A -0.338 0.234 0.040 -0.015 -0.003
## rc_thn_BOAA -0.183 0.087 0.017 0.028 0.015 0.130
## rc_thnc_HoL -0.275 0.201 -0.025 -0.040 0.030 0.169 0.127
## rc_th_O/U/D -0.382 0.254 0.028 0.045 -0.028 0.182 0.138 0.152
## clean_sexMl -0.219 0.082 0.033 -0.021 -0.166 0.003 0.037 -0.088 0.183
## cln_sxNn-Bn -0.079 -0.002 0.125 0.020 -0.022 0.057 0.047 0.042 0.068
## cln_sM
## demoEHR_Age
## dmEHR_DssDr
## ms_dx_cnPMS
## ms_d_MS,SNS
## rc_thncty_A
## rc_thn_BOAA
## rc_thnc_HoL
## rc_th_O/U/D
## clean_sexMl
## cln_sxNn-Bn 0.065# Residuals vs. fitted values
hist(resid(t25fw_fw_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(t25fw_fw_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
t25fw_fw_multi_dem_df <- tidy(t25fw_fw_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_fw_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_fw_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
t25fw_fw_multi_dem_df## # A tibble: 13 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Int~ 3.00e-1 0.0248 12.1 119. 1.72e-22 0.251
## 2 fixed <NA> demo~ -1.50e-3 0.000491 -3.05 121. 2.79e- 3 -0.00247
## 3 fixed <NA> demo~ 1.76e-4 0.000734 0.240 119. 8.11e- 1 -0.00128
## 4 fixed <NA> ms_d~ -6.41e-2 0.0145 -4.42 119. 2.22e- 5 -0.0929
## 5 fixed <NA> ms_d~ -3.68e-2 0.0593 -0.620 124. 5.36e- 1 -0.154
## 6 fixed <NA> race~ -4.92e-2 0.0195 -2.52 116. 1.30e- 2 -0.0879
## 7 fixed <NA> race~ -7.91e-2 0.0197 -4.01 115. 1.07e- 4 -0.118
## 8 fixed <NA> race~ -1.73e-2 0.0177 -0.974 116. 3.32e- 1 -0.0524
## 9 fixed <NA> race~ -7.76e-3 0.0201 -0.386 118. 7.00e- 1 -0.0476
## 10 fixed <NA> clea~ 1.05e-2 0.0119 0.882 116. 3.80e- 1 -0.0131
## 11 fixed <NA> clea~ -1.61e-2 0.0547 -0.294 99.6 7.69e- 1 -0.125
## 12 ran_pars bw_id sd__~ 5.03e-2 NA NA NA NA NA
## 13 ran_pars Residual sd__~ 2.65e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
t25fw_fw_multi_vid_unadj <- lmer(t25fw_inverse ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
(1|bw_id),
data = zeno_fw_clean_df)
summary(t25fw_fw_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## (1 | bw_id)
## Data: zeno_fw_clean_df
##
## REML criterion at convergence: -508.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0880 -0.4273 -0.0690 0.5028 3.3493
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.001364 0.03693
## Residual 0.001011 0.03180
## Number of obs: 167, groups: bw_id, 125
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.322e-01 5.949e-02 1.423e+02 5.584
## stride_time_median_sec_pose_zv -1.183e-01 3.255e-02 1.510e+02 -3.634
## log_delta_pix_h_rel_median_pose_zv 3.498e-02 7.595e-03 1.039e+02 4.605
## mean_cadence_step_per_min_pose_zv 3.545e-04 2.688e-04 1.244e+02 1.319
## stride_width_median_cm_pose_zv -1.367e-03 1.301e-03 1.241e+02 -1.050
## Pr(>|t|)
## (Intercept) 1.16e-07 ***
## stride_time_median_sec_pose_zv 0.000383 ***
## log_delta_pix_h_rel_median_pose_zv 1.17e-05 ***
## mean_cadence_step_per_min_pose_zv 0.189654
## stride_width_median_cm_pose_zv 0.295545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.838
## lg_dl______ -0.016 0.278
## mn_cdn_____ -0.897 0.634 -0.101
## strd_wd____ -0.262 0.031 0.233 0.022# Residuals vs. fitted values
hist(resid(t25fw_fw_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(t25fw_fw_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
t25fw_fw_multi_vid_unadj_df <- tidy(t25fw_fw_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_fw_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_fw_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
t25fw_fw_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inter~ 3.32e-1 0.0595 5.58 142. 1.16e-7 2.15e-1
## 2 fixed <NA> stride~ -1.18e-1 0.0325 -3.63 151. 3.83e-4 -1.83e-1
## 3 fixed <NA> log_de~ 3.50e-2 0.00760 4.61 104. 1.17e-5 1.99e-2
## 4 fixed <NA> mean_c~ 3.55e-4 0.000269 1.32 124. 1.90e-1 -1.78e-4
## 5 fixed <NA> stride~ -1.37e-3 0.00130 -1.05 124. 2.96e-1 -3.94e-3
## 6 ran_pars bw_id sd__(I~ 3.69e-2 NA NA NA NA NA
## 7 ran_pars Residual sd__Ob~ 3.18e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
t25fw_fw_multi_vid_adj <- lmer(t25fw_inverse ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_fw_clean_df)
summary(t25fw_fw_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_fw_clean_df
##
## REML criterion at convergence: -470.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9018 -0.4440 -0.0223 0.4637 3.4383
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0010421 0.03228
## Residual 0.0009064 0.03011
## Number of obs: 167, groups: bw_id, 125
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 3.772e-01 5.868e-02 1.470e+02
## stride_time_median_sec_pose_zv -1.035e-01 3.078e-02 1.484e+02
## log_delta_pix_h_rel_median_pose_zv 2.963e-02 7.286e-03 1.051e+02
## mean_cadence_step_per_min_pose_zv 4.035e-04 2.547e-04 1.272e+02
## stride_width_median_cm_pose_zv -3.059e-04 1.264e-03 1.208e+02
## demoEHR_Age -1.429e-03 3.773e-04 1.132e+02
## demoEHR_DiseaseDuration -2.995e-04 5.670e-04 9.727e+01
## ms_dx_condensedProgressive MS -1.332e-02 1.247e-02 1.086e+02
## ms_dx_condensedMS, Subtype Not Specified -4.429e-02 4.607e-02 1.240e+02
## race_ethnicity_cleanAsian -3.647e-02 1.504e-02 1.040e+02
## race_ethnicity_cleanBlack Or African American -5.929e-02 1.524e-02 1.034e+02
## race_ethnicity_cleanHispanic or Latino -1.321e-02 1.370e-02 1.036e+02
## race_ethnicity_cleanOther/Unknown/Declined -7.381e-03 1.539e-02 1.084e+02
## clean_sexMale 1.135e-02 9.166e-03 1.033e+02
## clean_sexNon-Binary -2.436e-02 3.959e-02 6.546e+01
## t value Pr(>|t|)
## (Intercept) 6.428 1.70e-09 ***
## stride_time_median_sec_pose_zv -3.364 0.000978 ***
## log_delta_pix_h_rel_median_pose_zv 4.066 9.25e-05 ***
## mean_cadence_step_per_min_pose_zv 1.584 0.115683
## stride_width_median_cm_pose_zv -0.242 0.809217
## demoEHR_Age -3.786 0.000247 ***
## demoEHR_DiseaseDuration -0.528 0.598536
## ms_dx_condensedProgressive MS -1.068 0.287934
## ms_dx_condensedMS, Subtype Not Specified -0.961 0.338201
## race_ethnicity_cleanAsian -2.425 0.017015 *
## race_ethnicity_cleanBlack Or African American -3.890 0.000177 ***
## race_ethnicity_cleanHispanic or Latino -0.964 0.337132
## race_ethnicity_cleanOther/Unknown/Declined -0.479 0.632561
## clean_sexMale 1.238 0.218344
## clean_sexNon-Binary -0.615 0.540458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Residuals vs. fitted values
hist(resid(t25fw_fw_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(t25fw_fw_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
t25fw_fw_multi_vid_adj_df <- tidy(t25fw_fw_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_fw_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_fw_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")
t25fw_fw_multi_vid_adj_df## # A tibble: 17 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 3.77e-1 0.0587 6.43 147. 1.70e-9 2.61e-1
## 2 fixed <NA> strid~ -1.04e-1 0.0308 -3.36 148. 9.78e-4 -1.64e-1
## 3 fixed <NA> log_d~ 2.96e-2 0.00729 4.07 105. 9.25e-5 1.52e-2
## 4 fixed <NA> mean_~ 4.03e-4 0.000255 1.58 127. 1.16e-1 -1.01e-4
## 5 fixed <NA> strid~ -3.06e-4 0.00126 -0.242 121. 8.09e-1 -2.81e-3
## 6 fixed <NA> demoE~ -1.43e-3 0.000377 -3.79 113. 2.47e-4 -2.18e-3
## 7 fixed <NA> demoE~ -3.00e-4 0.000567 -0.528 97.3 5.99e-1 -1.42e-3
## 8 fixed <NA> ms_dx~ -1.33e-2 0.0125 -1.07 109. 2.88e-1 -3.80e-2
## 9 fixed <NA> ms_dx~ -4.43e-2 0.0461 -0.961 124. 3.38e-1 -1.35e-1
## 10 fixed <NA> race_~ -3.65e-2 0.0150 -2.43 104. 1.70e-2 -6.63e-2
## 11 fixed <NA> race_~ -5.93e-2 0.0152 -3.89 103. 1.77e-4 -8.95e-2
## 12 fixed <NA> race_~ -1.32e-2 0.0137 -0.964 104. 3.37e-1 -4.04e-2
## 13 fixed <NA> race_~ -7.38e-3 0.0154 -0.479 108. 6.33e-1 -3.79e-2
## 14 fixed <NA> clean~ 1.14e-2 0.00917 1.24 103. 2.18e-1 -6.83e-3
## 15 fixed <NA> clean~ -2.44e-2 0.0396 -0.615 65.5 5.40e-1 -1.03e-1
## 16 ran_pars bw_id sd__(~ 3.23e-2 NA NA NA NA NA
## 17 ran_pars Residual sd__O~ 3.01e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>ANOVA - Compare Multivar Models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(t25fw_fw_multi_dem, t25fw_fw_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_fw_clean_df
## Models:
## t25fw_fw_multi_dem: t25fw_inverse ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## t25fw_fw_multi_vid_adj: t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## t25fw_fw_multi_dem 13 -509.29 -468.76 267.65 -535.29
## t25fw_fw_multi_vid_adj 17 -564.54 -511.53 299.27 -598.54 63.242 4
## Pr(>Chisq)
## t25fw_fw_multi_dem
## t25fw_fw_multi_vid_adj 6.035e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Video only vs Video + Demographics
anova(t25fw_fw_multi_vid_unadj, t25fw_fw_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_fw_clean_df
## Models:
## t25fw_fw_multi_vid_unadj: t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + (1 | bw_id)
## t25fw_fw_multi_vid_adj: t25fw_inverse ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## t25fw_fw_multi_vid_unadj 7 -543.38 -521.56 278.69 -557.38
## t25fw_fw_multi_vid_adj 17 -564.54 -511.53 299.27 -598.54 41.154 10
## Pr(>Chisq)
## t25fw_fw_multi_vid_unadj
## t25fw_fw_multi_vid_adj 1.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
t25fw_fw_multivar_all <- bind_rows(t25fw_fw_multi_dem_df,
t25fw_fw_multi_vid_unadj_df,
t25fw_fw_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- t25fw_fw_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.85 0.3
## 2 Unadjusted - Multivariate Video Metrics 0.74 0.4
## 3 Adjusted - Multivariate Video Metrics 0.79 0.55write.csv(adj_r_squared_df, file.path(output_dir, 't25fw_inverse_adj_vs_unadj_fw_r2.csv'))Plot Unadjusted vs Adjusted Estimates
p <- adj_vs_unadj_plot(results_df = t25fw_fw_multivar_all,
plot_title = "FW: Inverse T25FW Unadjusted vs Adjusted",
x_adj = 0.1)
p
ggsave(file.path(output_dir, 't25fw_inverse_adj_vs_unadj_FW_estimates.png'),
bg = "white", width= 12, height=10)Home Videos
Univariate Models
Outcome = Inverse Transform T25FW
uni_hv_t25fw_inv <- all_univariate_mixed_effect(data = home_df,
outcome_column = "t25fw_inverse")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 4 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ log_delta_pix_h_rel_median_pose + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: -222
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20740 -0.17872 -0.01406 0.19732 2.22353
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0037644 0.06135
## Residual 0.0002276 0.01509
## Number of obs: 61, groups: bw_id, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.222288 0.016966 57.894807 13.102 <2e-16
## log_delta_pix_h_rel_median_pose 0.016957 0.009155 42.952256 1.852 0.0709
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.741
## [1] "stride_time_median_sec_pose"## Warning: Removed 13 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: -190.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05158 -0.13872 -0.02463 0.13797 2.15674
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.004071 0.06380
## Residual 0.000259 0.01609
## Number of obs: 52, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.21665 0.04428 49.21227 4.893 1.11e-05 ***
## stride_time_median_sec_pose -0.01337 0.03531 49.99306 -0.379 0.707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.952
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 12 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ mean_cadence_step_per_min_pose + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: -186.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09674 -0.14982 -0.04066 0.12612 2.23609
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0041095 0.06411
## Residual 0.0002448 0.01565
## Number of obs: 53, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.849e-01 2.458e-02 4.931e+01 7.525 9.86e-10
## mean_cadence_step_per_min_pose 1.539e-04 1.991e-04 3.175e+01 0.773 0.445
##
## (Intercept) ***
## mean_cadence_step_per_min_pose
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.834
## [1] "stride_width_median_cm_pose"## Warning: Removed 12 rows containing missing values (`geom_point()`).
## t25fw_inverse ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: -193
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.36921 -0.18866 0.00824 0.18918 2.19279
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0037315 0.06109
## Residual 0.0002433 0.01560
## Number of obs: 53, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.233644 0.022924 50.290046 10.192 7.9e-14 ***
## stride_width_median_cm_pose -0.002488 0.001433 32.908115 -1.736 0.0919 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.826
## [1] "demoEHR_Age"
## t25fw_inverse ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: -241.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.28072 -0.07812 -0.00165 0.10099 2.35700
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0033466 0.05785
## Residual 0.0002077 0.01441
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.3100175 0.0449534 28.8346954 6.896 1.45e-07 ***
## demoEHR_Age -0.0021324 0.0008394 28.8471630 -2.540 0.0167 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.972
## [1] "demoEHR_DiseaseDuration"
## t25fw_inverse ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: -238.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16953 -0.10488 -0.00581 0.09556 2.32969
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0037911 0.06157
## Residual 0.0002057 0.01434
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.224684 0.019091 29.854249 11.769 9.69e-13 ***
## demoEHR_DiseaseDuration -0.002317 0.001394 30.126294 -1.661 0.107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.809
## [1] "ms_dx_condensed"
## t25fw_inverse ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: -245.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26388 -0.10870 -0.00785 0.12494 2.36993
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0032602 0.05710
## Residual 0.0002081 0.01443
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.211994 0.011854 27.885494 17.884
## ms_dx_condensedProgressive MS -0.081647 0.028591 28.055887 -2.856
## ms_dx_condensedMS, Subtype Not Specified 0.002292 0.042693 27.772130 0.054
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ms_dx_condensedProgressive MS 0.00799 **
## ms_dx_condensedMS, Subtype Not Specified 0.95757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.415
## ms_d_MS,SNS -0.278 0.115
## [1] "race_ethnicity_clean"
## t25fw_inverse ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: -238.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.30639 -0.09202 0.00000 0.10448 2.33295
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0035069 0.05922
## Residual 0.0002076 0.01441
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.19764 0.01229 26.09424
## race_ethnicity_cleanAsian 0.03139 0.04423 25.93546
## race_ethnicity_cleanBlack Or African American -0.13353 0.06133 25.92911
## race_ethnicity_cleanHispanic or Latino -0.03635 0.06133 25.92911
## race_ethnicity_cleanOther/Unknown/Declined 0.04994 0.03680 25.90407
## t value Pr(>|t|)
## (Intercept) 16.085 4.63e-15 ***
## race_ethnicity_cleanAsian 0.710 0.4842
## race_ethnicity_cleanBlack Or African American -2.177 0.0388 *
## race_ethnicity_cleanHispanic or Latino -0.593 0.5586
## race_ethnicity_cleanOther/Unknown/Declined 1.357 0.1865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.278
## rc_thn_BOAA -0.200 0.056
## rc_thnc_HoL -0.200 0.056 0.040
## rc_th_O/U/D -0.334 0.093 0.067 0.067
## [1] "clean_sex"
## t25fw_inverse ~ clean_sex + (1 | bw_id)
## <environment: 0x0000000030987390>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: -240.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25613 -0.09814 -0.00234 0.07580 2.38367
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0039414 0.06278
## Residual 0.0002076 0.01441
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.18999 0.01300 28.06524 14.620 1.19e-14 ***
## clean_sexMale 0.04369 0.02908 28.16454 1.502 0.144
## clean_sexNon-Binary 0.01834 0.06492 27.96449 0.283 0.780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.447
## cln_sxNn-Bn -0.200 0.089
# save adjusted r_squared
adj_r_squared_df <- uni_hv_t25fw_inv[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.94 0.02
## 2 Stride Time - Median 0.94 0
## 3 Cadence - Mean 0.94 0
## 4 Stride Width - Median 0.94 0.01
## 5 Age 0.95 0.18
## 6 Disease Duration 0.95 0.08
## 7 MS DX 0.95 0.19
## 8 Race and Ethnicity 0.96 0.21
## 9 Sex 0.95 0.06write.csv(adj_r_squared_df, file.path(output_dir, 't25fw_inverse_univariate_home_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_hv_t25fw_inv,
plot_title = "Home Univariate - lmer(Inverse T25FW ~ Predictor + (1 | bw_id))",
x_adj = 0.1)
p 
ggsave(file.path(output_dir, 't25fw_inverse_univariate_home_estimates.png'),
bg = "white", width= 10, height=10)Drop rows with any missing video metrics
Not all metrics could be calculated from each video metric
Dropped videos missing any video metric so models could be compared
home_clean_df <- home_df %>%
drop_na(c("stride_time_median_sec_pose_hv",
"log_delta_pix_h_rel_median_pose_hv",
"mean_cadence_step_per_min_pose_hv",
"stride_width_median_cm_pose_hv"))
colSums(is.na(home_clean_df))## X
## 0
## video_id_date_name_pose_hv
## 0
## id_date_pose_hv
## 0
## task_pose_hv
## 0
## frames_per_second_pose_hv
## 0
## total_video_duration_sec_pose_hv
## 0
## delta_pix_h_rel_median_pose_hv
## 0
## walking_segmets_n_pose_hv
## 0
## walking_segments_duration_mean_pose_hv
## 0
## walking_segments_duration_median_pose_hv
## 0
## stride_time_mean_sec_pose_hv
## 0
## stride_time_median_sec_pose_hv
## 0
## stride_time_std_pose_hv
## 1
## stride_time_cv_pose_hv
## 1
## stride_time_max_pose_hv
## 0
## stride_time_min_pose_hv
## 0
## mean_cadence_step_per_min_pose_hv
## 0
## stride_width_mean_cm_pose_hv
## 0
## stride_width_median_cm_pose_hv
## 0
## stride_width_std_pose_hv
## 0
## stride_width_cv_pose_hv
## 0
## stride_width_max_pose_hv
## 0
## stride_width_min_pose_hv
## 0
## foot1_gait_cycle_time_mean_pose_hv
## 21
## foot1_stance_time_mean_pose_hv
## 21
## foot1_stance_per_mean_pose_hv
## 21
## foot1_swing_time_mean_pose_hv
## 21
## foot1_swing_per_mean_pose_hv
## 21
## foot1_ini_double_support_time_mean_pose_hv
## 21
## foot1_term_double_support_time_mean_pose_hv
## 21
## foot1_tot_double_support_time_mean_pose_hv
## 21
## foot1_double_support_per_mean_pose_hv
## 21
## foot1_single_support_time_mean_pose_hv
## 21
## foot1_single_support_per_mean_pose_hv
## 21
## walk_segment_pose_hv
## 21
## foot1_pose_hv
## 0
## id_video
## 0
## visit_date_video
## 0
## bw_id
## 0
## trialdate
## 0
## visit_date
## 0
## demoGait_dateDiff
## 0
## demoEHR_DiseaseDuration
## 0
## clean_sex
## 0
## clean_race
## 0
## clean_ethnicity
## 0
## demoEHR_Age
## 0
## bingoEHR_DX_MS.DX
## 0
## bingoEHR_EDSS_measure_value
## 0
## msfcEHR_T25FW.SPEED.AVG
## 0
## FW_cadencestepsminmean
## 0
## FW_singlesupportmean
## 0
## FW_singlesupportratiolr
## 0
## FW_stridetimeseccv
## 0
## FW_stridetimesecmean
## 0
## FW_stridewidthcmmean
## 0
## FW_stridewidthcmsd
## 0
## FW_totaldsupportmean
## 0
## FW_totaldsupportratiolr
## 0
## FW_velocitycmsecmean
## 0
## PWS_cadencestepsminmean
## 0
## PWS_singlesupportmean
## 0
## PWS_singlesupportratiolr
## 0
## PWS_stridetimeseccv
## 0
## PWS_stridetimesecmean
## 0
## PWS_stridewidthcmmean
## 0
## PWS_stridewidthcmsd
## 0
## PWS_totaldsupportmean
## 0
## PWS_totaldsupportratiolr
## 0
## PWS_velocitycmsecmean
## 0
## redcap_event_name
## 0
## demographic_diagnosis
## 0
## msfcEHR_T25FW.SPEED.TRIAL.1.vDate.Diff
## 0
## msfcEHR_T25FW.SPEED.TRIAL.2.vDate.Diff
## 6
## demoEHR_DX_dateDiff
## 0
## demoEHR_EDSS_dateDiff
## 0
## bingoEHR_Vitals_height.in.
## 4
## demoEHR_Vitals_dateDiff
## 0
## bingoEHR_first_MS.DX
## 0
## edss_severity_num
## 0
## edss_severity_cat
## 0
## t25fw_group_num
## 0
## t25fw_group_cat
## 0
## race_ethnicity_clean
## 0
## ms_dx_condensed
## 0
## bw_hv_date_diff_days
## 0
## t25fw_log
## 0
## t25fw_inverse
## 0
##
## 0
## log_delta_pix_h_rel_median_pose_hv
## 0Multivariate Demographics and MS Info
t25fw_home_multi_dem <- lmer(t25fw_inverse ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = home_clean_df)
summary(t25fw_home_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: -164.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0084 -0.1560 0.0000 0.1933 2.1302
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.001546 0.03932
## Residual 0.000251 0.01584
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.2508142 0.0477598 13.8806002
## demoEHR_Age -0.0007275 0.0009284 14.1597856
## demoEHR_DiseaseDuration -0.0012726 0.0013736 14.7217691
## ms_dx_condensedProgressive MS -0.1011598 0.0243464 14.0431223
## ms_dx_condensedMS, Subtype Not Specified 0.0096999 0.0353248 14.0674519
## race_ethnicity_cleanAsian 0.0731286 0.0460612 13.9157408
## race_ethnicity_cleanHispanic or Latino -0.0069675 0.0477163 14.0560319
## race_ethnicity_cleanOther/Unknown/Declined 0.0416148 0.0305952 14.1273505
## clean_sexMale 0.0515676 0.0243978 14.4804702
## t value Pr(>|t|)
## (Intercept) 5.252 0.000126 ***
## demoEHR_Age -0.784 0.446169
## demoEHR_DiseaseDuration -0.926 0.369161
## ms_dx_condensedProgressive MS -4.155 0.000966 ***
## ms_dx_condensedMS, Subtype Not Specified 0.275 0.787621
## race_ethnicity_cleanAsian 1.588 0.134823
## race_ethnicity_cleanHispanic or Latino -0.146 0.885980
## race_ethnicity_cleanOther/Unknown/Declined 1.360 0.195085
## clean_sexMale 2.114 0.052348 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__HoL r__O/U
## demoEHR_Age -0.936
## dmEHR_DssDr 0.241 -0.470
## ms_dx_cnPMS 0.090 -0.188 -0.047
## ms_d_MS,SNS 0.308 -0.328 -0.103 0.270
## rc_thncty_A -0.424 0.341 -0.048 0.041 -0.082
## rc_thnc_HoL 0.056 -0.035 -0.369 0.194 0.204 0.010
## rc_th_O/U/D -0.522 0.461 -0.317 0.118 -0.017 0.263 0.149
## clean_sexMl -0.488 0.429 -0.260 -0.074 -0.264 0.243 0.116 0.364# Residuals vs. fitted values
hist(resid(t25fw_home_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(t25fw_home_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
t25fw_home_multi_dem_df <- tidy(t25fw_home_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_home_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_home_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
t25fw_home_multi_dem_df## # A tibble: 11 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 2.51e-1 0.0478 5.25 13.9 1.26e-4 1.48e-1
## 2 fixed <NA> demoE~ -7.27e-4 0.000928 -0.784 14.2 4.46e-1 -2.72e-3
## 3 fixed <NA> demoE~ -1.27e-3 0.00137 -0.926 14.7 3.69e-1 -4.21e-3
## 4 fixed <NA> ms_dx~ -1.01e-1 0.0243 -4.16 14.0 9.66e-4 -1.53e-1
## 5 fixed <NA> ms_dx~ 9.70e-3 0.0353 0.275 14.1 7.88e-1 -6.60e-2
## 6 fixed <NA> race_~ 7.31e-2 0.0461 1.59 13.9 1.35e-1 -2.57e-2
## 7 fixed <NA> race_~ -6.97e-3 0.0477 -0.146 14.1 8.86e-1 -1.09e-1
## 8 fixed <NA> race_~ 4.16e-2 0.0306 1.36 14.1 1.95e-1 -2.39e-2
## 9 fixed <NA> clean~ 5.16e-2 0.0244 2.11 14.5 5.23e-2 -5.98e-4
## 10 ran_pars bw_id sd__(~ 3.93e-2 NA NA NA NA NA
## 11 ran_pars Residual sd__O~ 1.58e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
t25fw_home_multi_vid_unadj <- lmer(t25fw_inverse ~
stride_time_median_sec_pose_hv +
log_delta_pix_h_rel_median_pose_hv +
mean_cadence_step_per_min_pose_hv +
stride_width_median_cm_pose_hv +
(1|bw_id),
data = home_clean_df)
summary(t25fw_home_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv +
## mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv +
## (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: -158.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9388 -0.3147 -0.0122 0.3599 2.0351
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.0029432 0.05425
## Residual 0.0003085 0.01757
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.2477041 0.0648192 40.7298969 3.821
## stride_time_median_sec_pose_hv -0.0205564 0.0382025 45.9439266 -0.538
## log_delta_pix_h_rel_median_pose_hv 0.0150123 0.0154764 26.4420943 0.970
## mean_cadence_step_per_min_pose_hv 0.0002833 0.0002729 22.0126866 1.038
## stride_width_median_cm_pose_hv -0.0025538 0.0019784 30.4994112 -1.291
## Pr(>|t|)
## (Intercept) 0.000445 ***
## stride_time_median_sec_pose_hv 0.593113
## log_delta_pix_h_rel_median_pose_hv 0.340830
## mean_cadence_step_per_min_pose_hv 0.310395
## stride_width_median_cm_pose_hv 0.206445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.819
## lg_dl______ -0.110 0.308
## mn_cdn_____ -0.629 0.282 -0.076
## strd_wd____ -0.361 0.197 0.526 -0.076# Residuals vs. fitted values
hist(resid(t25fw_home_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(t25fw_home_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
t25fw_home_multi_vid_unadj_df <- tidy(t25fw_home_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_home_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_home_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
t25fw_home_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inter~ 2.48e-1 0.0648 3.82 40.7 4.45e-4 1.17e-1
## 2 fixed <NA> stride~ -2.06e-2 0.0382 -0.538 45.9 5.93e-1 -9.75e-2
## 3 fixed <NA> log_de~ 1.50e-2 0.0155 0.970 26.4 3.41e-1 -1.68e-2
## 4 fixed <NA> mean_c~ 2.83e-4 0.000273 1.04 22.0 3.10e-1 -2.83e-4
## 5 fixed <NA> stride~ -2.55e-3 0.00198 -1.29 30.5 2.06e-1 -6.59e-3
## 6 ran_pars bw_id sd__(I~ 5.43e-2 NA NA NA NA NA
## 7 ran_pars Residual sd__Ob~ 1.76e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
t25fw_home_multi_vid_adj <- lmer(t25fw_inverse ~
stride_time_median_sec_pose_hv +
log_delta_pix_h_rel_median_pose_hv +
mean_cadence_step_per_min_pose_hv +
stride_width_median_cm_pose_hv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = home_clean_df)
summary(t25fw_home_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## t25fw_inverse ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv +
## mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: -131.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93370 -0.24197 -0.01113 0.21746 2.11490
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 0.001607 0.04009
## Residual 0.000242 0.01556
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.1866375 0.0821905 31.4403808
## stride_time_median_sec_pose_hv 0.0471890 0.0394827 37.9992439
## log_delta_pix_h_rel_median_pose_hv 0.0133408 0.0137909 30.2599418
## mean_cadence_step_per_min_pose_hv 0.0002295 0.0002397 28.0222212
## stride_width_median_cm_pose_hv -0.0011244 0.0018176 31.8904406
## demoEHR_Age -0.0004608 0.0009626 11.8621290
## demoEHR_DiseaseDuration -0.0011184 0.0014161 12.4423293
## ms_dx_condensedProgressive MS -0.1087408 0.0333150 21.3335450
## ms_dx_condensedMS, Subtype Not Specified 0.0019466 0.0376499 12.6659284
## race_ethnicity_cleanAsian 0.0803845 0.0471646 11.2833325
## race_ethnicity_cleanHispanic or Latino -0.0098782 0.0490510 11.5528452
## race_ethnicity_cleanOther/Unknown/Declined 0.0429345 0.0312136 11.3476499
## clean_sexMale 0.0550775 0.0255898 12.7789728
## t value Pr(>|t|)
## (Intercept) 2.271 0.03015 *
## stride_time_median_sec_pose_hv 1.195 0.23942
## log_delta_pix_h_rel_median_pose_hv 0.967 0.34103
## mean_cadence_step_per_min_pose_hv 0.957 0.34669
## stride_width_median_cm_pose_hv -0.619 0.54056
## demoEHR_Age -0.479 0.64084
## demoEHR_DiseaseDuration -0.790 0.44446
## ms_dx_condensedProgressive MS -3.264 0.00365 **
## ms_dx_condensedMS, Subtype Not Specified 0.052 0.95957
## race_ethnicity_cleanAsian 1.704 0.11567
## race_ethnicity_cleanHispanic or Latino -0.201 0.84390
## race_ethnicity_cleanOther/Unknown/Declined 1.376 0.19552
## clean_sexMale 2.152 0.05109 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Residuals vs. fitted values
hist(resid(t25fw_home_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(t25fw_home_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
t25fw_home_multi_vid_adj_df <- tidy(t25fw_home_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(t25fw_home_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(t25fw_home_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")
t25fw_home_multi_vid_adj_df## # A tibble: 15 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 1.87e-1 0.0822 2.27 31.4 0.0302 1.91e-2
## 2 fixed <NA> strid~ 4.72e-2 0.0395 1.20 38.0 0.239 -3.27e-2
## 3 fixed <NA> log_d~ 1.33e-2 0.0138 0.967 30.3 0.341 -1.48e-2
## 4 fixed <NA> mean_~ 2.29e-4 0.000240 0.957 28.0 0.347 -2.62e-4
## 5 fixed <NA> strid~ -1.12e-3 0.00182 -0.619 31.9 0.541 -4.83e-3
## 6 fixed <NA> demoE~ -4.61e-4 0.000963 -0.479 11.9 0.641 -2.56e-3
## 7 fixed <NA> demoE~ -1.12e-3 0.00142 -0.790 12.4 0.444 -4.19e-3
## 8 fixed <NA> ms_dx~ -1.09e-1 0.0333 -3.26 21.3 0.00365 -1.78e-1
## 9 fixed <NA> ms_dx~ 1.95e-3 0.0376 0.0517 12.7 0.960 -7.96e-2
## 10 fixed <NA> race_~ 8.04e-2 0.0472 1.70 11.3 0.116 -2.31e-2
## 11 fixed <NA> race_~ -9.88e-3 0.0491 -0.201 11.6 0.844 -1.17e-1
## 12 fixed <NA> race_~ 4.29e-2 0.0312 1.38 11.3 0.196 -2.55e-2
## 13 fixed <NA> clean~ 5.51e-2 0.0256 2.15 12.8 0.0511 -3.03e-4
## 14 ran_pars bw_id sd__(~ 4.01e-2 NA NA NA NA NA
## 15 ran_pars Residual sd__O~ 1.56e-2 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>ANOVA compare multivariate models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(t25fw_home_multi_dem, t25fw_home_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: home_clean_df
## Models:
## t25fw_home_multi_dem: t25fw_inverse ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## t25fw_home_multi_vid_adj: t25fw_inverse ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## t25fw_home_multi_dem 11 -207.16 -185.91 114.58 -229.16
## t25fw_home_multi_vid_adj 15 -204.58 -175.60 117.29 -234.58 5.4226 4
## Pr(>Chisq)
## t25fw_home_multi_dem
## t25fw_home_multi_vid_adj 0.2466# Video only vs Video + Demographics
anova(t25fw_home_multi_vid_unadj, t25fw_home_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: home_clean_df
## Models:
## t25fw_home_multi_vid_unadj: t25fw_inverse ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + (1 | bw_id)
## t25fw_home_multi_vid_adj: t25fw_inverse ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## t25fw_home_multi_vid_unadj 7 -189.08 -175.56 101.54 -203.08
## t25fw_home_multi_vid_adj 15 -204.58 -175.60 117.29 -234.58 31.505 8
## Pr(>Chisq)
## t25fw_home_multi_vid_unadj
## t25fw_home_multi_vid_adj 0.0001142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
t25fw_home_multivar_all <- bind_rows(t25fw_home_multi_dem_df,
t25fw_home_multi_vid_unadj_df,
t25fw_home_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- t25fw_home_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.95 0.62
## 2 Unadjusted - Multivariate Video Metrics 0.91 0.08
## 3 Adjusted - Multivariate Video Metrics 0.95 0.62write.csv(adj_r_squared_df, file.path(output_dir, 't25fw_inverse_adj_vs_unadj_home_r2.csv'))Plot Unadjusted vs Adjusted
p <- adj_vs_unadj_plot(results_df = t25fw_home_multivar_all,
plot_title = "Home: Inverse T25FW Unadjusted vs Adjusted",
x_adj = 0.1)
p
ggsave(file.path(output_dir, 't25fw_inverse_adj_vs_unadj_home_estimates.png'),
bg = "white", width= 12, height=10)Zeno Mat Velocity Mixed Effect Model
Plot and transform Zeno mat velocity
Preferred Walking Speed
# preferred walking speed
ggplot(data = zeno_pws_df, mapping = aes(PWS_velocitycmsecmean)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# log
zeno_pws_df$log_PWS_velocity <- log(zeno_pws_df$PWS_velocitycmsecmean)
ggplot(data = zeno_pws_df, mapping = aes(log_PWS_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# inverse
zeno_pws_df$inv_PWS_velocity <- 1 / zeno_pws_df$PWS_velocitycmsecmean
ggplot(data = zeno_pws_df, mapping = aes(inv_PWS_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fast Walking Speed
# fast walking speed
ggplot(data = zeno_fw_df, mapping = aes(FW_velocitycmsecmean)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# log
zeno_fw_df$log_FW_velocity <- log(zeno_fw_df$FW_velocitycmsecmean)
ggplot(data = zeno_fw_df, mapping = aes(log_FW_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# inverse
zeno_fw_df$inv_FW_velocity <- 1 / zeno_fw_df$FW_velocitycmsecmean
ggplot(data = zeno_fw_df, mapping = aes(inv_FW_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# square root
zeno_fw_df$sqrt_FW_velocity <- sqrt(zeno_fw_df$FW_velocitycmsecmean)
ggplot(data = zeno_fw_df, mapping = aes(sqrt_FW_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Home Videos - most recent PWS
# fast walking speed
ggplot(data = home_df, mapping = aes(PWS_velocitycmsecmean)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# log
home_df$log_PWS_velocity <- log(home_df$PWS_velocitycmsecmean)
ggplot(data = home_df, mapping = aes(log_PWS_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# inverse
home_df$inv_PWS_velocity <- 1 / home_df$PWS_velocitycmsecmean
ggplot(data = home_df, mapping = aes(inv_PWS_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# square root
home_df$sqrt_PWS_velocity <- sqrt(home_df$PWS_velocitycmsecmean)
ggplot(data = home_df, mapping = aes(sqrt_PWS_velocity)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Preferred Walking speed videos
Univariate Models
uni_zv_pws_pwsvel <- all_univariate_mixed_effect(data = zeno_pws_df,
outcome_column = "PWS_velocitycmsecmean")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 16 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ log_delta_pix_h_rel_median_pose + (1 |
## bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: 1913
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.66513 -0.45718 0.06148 0.43131 1.82904
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 489.3 22.12
## Residual 225.2 15.01
## Number of obs: 208, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 128.053 4.925 181.126 26.000 < 2e-16
## log_delta_pix_h_rel_median_pose 18.038 3.134 150.731 5.755 4.7e-08
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.906
## [1] "stride_time_median_sec_pose"## Warning: Removed 48 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: 1582.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73475 -0.46264 0.08608 0.52814 2.10338
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 275.9 16.61
## Residual 271.1 16.46
## Number of obs: 176, groups: bw_id, 128
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 180.45 12.18 144.58 14.815 < 2e-16 ***
## stride_time_median_sec_pose -67.85 10.66 140.48 -6.365 2.59e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.987
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 40 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ mean_cadence_step_per_min_pose + (1 |
## bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: 1663.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9411 -0.4531 0.0404 0.5730 1.4936
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 334.5 18.29
## Residual 232.7 15.25
## Number of obs: 184, groups: bw_id, 132
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.6310 11.8367 171.3552 1.405 0.162
## mean_cadence_step_per_min_pose 0.8334 0.1130 169.2633 7.376 6.96e-12
##
## (Intercept)
## mean_cadence_step_per_min_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.986
## [1] "stride_width_median_cm_pose"## Warning: Removed 40 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: 1701.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.00080 -0.39103 0.07425 0.47990 1.74120
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 502.0 22.41
## Residual 245.4 15.66
## Number of obs: 184, groups: bw_id, 132
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 116.9113 7.2951 173.5963 16.026 <2e-16 ***
## stride_width_median_cm_pose -1.1348 0.5514 164.1555 -2.058 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.949
## [1] "demoEHR_Age"
## PWS_velocitycmsecmean ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: 2076.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94413 -0.36526 0.04794 0.34376 1.89415
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 666.5 25.82
## Residual 187.3 13.69
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 126.5386 9.2830 153.9318 13.631 < 2e-16 ***
## demoEHR_Age -0.4796 0.1791 154.7347 -2.677 0.00823 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.969
## [1] "demoEHR_DiseaseDuration"
## PWS_velocitycmsecmean ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: 2081.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9000 -0.3766 0.0352 0.3760 1.9269
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 694.0 26.34
## Residual 188.9 13.74
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 105.5672 3.7214 153.9227 28.368 <2e-16 ***
## demoEHR_DiseaseDuration -0.3184 0.2964 153.7503 -1.074 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.779
## [1] "ms_dx_condensed"
## PWS_velocitycmsecmean ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: 2024.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.00088 -0.38645 0.03448 0.38824 1.94371
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 504.4 22.46
## Residual 182.8 13.52
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 109.276 2.287 152.631 47.790
## ms_dx_condensedProgressive MS -37.122 5.262 155.734 -7.055
## ms_dx_condensedMS, Subtype Not Specified 11.522 18.010 150.601 0.640
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ms_dx_condensedProgressive MS 5.33e-11 ***
## ms_dx_condensedMS, Subtype Not Specified 0.523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.435
## ms_d_MS,SNS -0.127 0.055
## [1] "race_ethnicity_clean"
## PWS_velocitycmsecmean ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: 2051.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90162 -0.35360 0.02896 0.38133 1.94146
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 687.1 26.21
## Residual 187.1 13.68
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 102.9381 2.8117 148.0586
## race_ethnicity_cleanAsian 4.0172 9.1090 146.5408
## race_ethnicity_cleanBlack Or African American -20.2064 9.2122 153.5385
## race_ethnicity_cleanHispanic or Latino 0.5128 7.9700 149.4285
## race_ethnicity_cleanOther/Unknown/Declined 7.6193 8.7771 147.8014
## t value Pr(>|t|)
## (Intercept) 36.610 <2e-16 ***
## race_ethnicity_cleanAsian 0.441 0.6598
## race_ethnicity_cleanBlack Or African American -2.193 0.0298 *
## race_ethnicity_cleanHispanic or Latino 0.064 0.9488
## race_ethnicity_cleanOther/Unknown/Declined 0.868 0.3868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.309
## rc_thn_BOAA -0.305 0.094
## rc_thnc_HoL -0.353 0.109 0.108
## rc_th_O/U/D -0.320 0.099 0.098 0.113
## [1] "clean_sex"
## PWS_velocitycmsecmean ~ clean_sex + (1 | bw_id)
## <environment: 0x00000000207067c0>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: 2067.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90390 -0.35516 0.03233 0.37135 1.97743
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 710.2 26.65
## Residual 186.6 13.66
## Number of obs: 224, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 102.1214 2.7247 150.6754 37.480 <2e-16 ***
## clean_sexMale 0.8422 5.4739 151.4409 0.154 0.878
## clean_sexNon-Binary 17.3286 28.4765 133.6691 0.609 0.544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.498
## cln_sxNn-Bn -0.096 0.048
# save adjusted r_squared
adj_r_squared_df <- uni_zv_pws_pwsvel[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.72 0.11
## 2 Stride Time - Median 0.59 0.17
## 3 Cadence - Mean 0.68 0.21
## 4 Stride Width - Median 0.68 0.02
## 5 Age 0.79 0.04
## 6 Disease Duration 0.79 0.01
## 7 MS DX 0.8 0.23
## 8 Race and Ethnicity 0.79 0.03
## 9 Sex 0.79 0write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelPws_univariate_pws_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_zv_pws_pwsvel,
plot_title = "PWS Univariate - lmer(Zeno Mat Velocity ~ Predictor + (1 | bw_id))",
x_adj = 15)
p 
ggsave(file.path(output_dir, 'ZenoVelPws_univariate_pws_estimates.png'),
bg = "white", width= 10, height=10)Multivariate Demographics and MS Info
pwsVel_pws_multi_dem <- lmer(PWS_velocitycmsecmean ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_pws_clean_df)
summary(pwsVel_pws_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PWS_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration +
## ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_pws_clean_df
##
## REML criterion at convergence: 1416.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98899 -0.41289 0.05874 0.44493 1.73443
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 436.0 20.88
## Residual 189.8 13.78
## Number of obs: 162, groups: bw_id, 127
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 119.11391 10.86298 115.42984
## demoEHR_Age -0.21920 0.22404 118.39410
## demoEHR_DiseaseDuration 0.12505 0.31794 114.91096
## ms_dx_condensedProgressive MS -28.93353 6.36640 117.89377
## ms_dx_condensedMS, Subtype Not Specified 15.48659 18.29132 125.53293
## race_ethnicity_cleanAsian 0.07824 9.37501 117.49770
## race_ethnicity_cleanBlack Or African American -25.61581 8.56468 111.51624
## race_ethnicity_cleanHispanic or Latino 2.50555 7.63177 114.69685
## race_ethnicity_cleanOther/Unknown/Declined -5.99989 9.65149 118.77113
## clean_sexMale 1.02687 5.07064 117.25922
## clean_sexNon-Binary 6.73315 25.37795 125.89521
## t value Pr(>|t|)
## (Intercept) 10.965 < 2e-16 ***
## demoEHR_Age -0.978 0.32987
## demoEHR_DiseaseDuration 0.393 0.69482
## ms_dx_condensedProgressive MS -4.545 1.34e-05 ***
## ms_dx_condensedMS, Subtype Not Specified 0.847 0.39879
## race_ethnicity_cleanAsian 0.008 0.99336
## race_ethnicity_cleanBlack Or African American -2.991 0.00342 **
## race_ethnicity_cleanHispanic or Latino 0.328 0.74328
## race_ethnicity_cleanOther/Unknown/Declined -0.622 0.53536
## clean_sexMale 0.203 0.83987
## clean_sexNon-Binary 0.265 0.79120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__OAA r__HoL r__O/U
## demoEHR_Age -0.920
## dmEHR_DssDr 0.185 -0.457
## ms_dx_cnPMS 0.120 -0.180 -0.119
## ms_d_MS,SNS 0.129 -0.142 -0.045 0.114
## rc_thncty_A -0.286 0.217 -0.032 -0.012 0.007
## rc_thn_BOAA -0.162 0.075 -0.005 0.040 0.032 0.106
## rc_thnc_HoL -0.313 0.236 -0.047 0.032 0.023 0.166 0.128
## rc_th_O/U/D -0.351 0.252 -0.032 0.052 -0.009 0.136 0.117 0.153
## clean_sexMl -0.151 0.030 0.002 0.002 -0.075 -0.052 0.028 -0.127 0.142
## cln_sxNn-Bn -0.055 -0.012 0.106 0.022 0.002 0.034 0.039 0.038 0.048
## cln_sM
## demoEHR_Age
## dmEHR_DssDr
## ms_dx_cnPMS
## ms_d_MS,SNS
## rc_thncty_A
## rc_thn_BOAA
## rc_thnc_HoL
## rc_th_O/U/D
## clean_sexMl
## cln_sxNn-Bn 0.052# Residuals vs. fitted values
hist(resid(pwsVel_pws_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(pwsVel_pws_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
pwsVel_pws_multi_dem_df <- tidy(pwsVel_pws_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(pwsVel_pws_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(pwsVel_pws_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
pwsVel_pws_multi_dem_df## # A tibble: 13 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Int~ 119. 10.9 11.0 115. 1.33e-19 97.6
## 2 fixed <NA> demo~ -0.219 0.224 -0.978 118. 3.30e- 1 -0.663
## 3 fixed <NA> demo~ 0.125 0.318 0.393 115. 6.95e- 1 -0.505
## 4 fixed <NA> ms_d~ -28.9 6.37 -4.54 118. 1.34e- 5 -41.5
## 5 fixed <NA> ms_d~ 15.5 18.3 0.847 126. 3.99e- 1 -20.7
## 6 fixed <NA> race~ 0.0782 9.38 0.00835 117. 9.93e- 1 -18.5
## 7 fixed <NA> race~ -25.6 8.56 -2.99 112. 3.42e- 3 -42.6
## 8 fixed <NA> race~ 2.51 7.63 0.328 115. 7.43e- 1 -12.6
## 9 fixed <NA> race~ -6.00 9.65 -0.622 119. 5.35e- 1 -25.1
## 10 fixed <NA> clea~ 1.03 5.07 0.203 117. 8.40e- 1 -9.02
## 11 fixed <NA> clea~ 6.73 25.4 0.265 126. 7.91e- 1 -43.5
## 12 ran_pars bw_id sd__~ 20.9 NA NA NA NA NA
## 13 ran_pars Residual sd__~ 13.8 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
pwsVel_pws_multi_vid_unadj <- lmer(PWS_velocitycmsecmean ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
(1|bw_id),
data = zeno_pws_clean_df)
summary(pwsVel_pws_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PWS_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## (1 | bw_id)
## Data: zeno_pws_clean_df
##
## REML criterion at convergence: 1431.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86529 -0.45371 0.05271 0.54598 1.30773
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 262.4 16.20
## Residual 222.8 14.92
## Number of obs: 162, groups: bw_id, 127
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 116.1956 32.0769 107.4974 3.622
## stride_time_median_sec_pose_zv -44.4045 14.5072 97.6776 -3.061
## log_delta_pix_h_rel_median_pose_zv 2.1329 3.5858 118.9712 0.595
## mean_cadence_step_per_min_pose_zv 0.5638 0.1718 111.2112 3.281
## stride_width_median_cm_pose_zv -1.4834 0.5452 156.6905 -2.721
## Pr(>|t|)
## (Intercept) 0.000447 ***
## stride_time_median_sec_pose_zv 0.002850 **
## log_delta_pix_h_rel_median_pose_zv 0.553106
## mean_cadence_step_per_min_pose_zv 0.001381 **
## stride_width_median_cm_pose_zv 0.007248 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.811
## lg_dl______ 0.102 0.205
## mn_cdn_____ -0.903 0.582 -0.121
## strd_wd____ -0.286 0.037 0.099 0.124# Residuals vs. fitted values
hist(resid(pwsVel_pws_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(pwsVel_pws_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
pwsVel_pws_multi_vid_unadj_df <- tidy(pwsVel_pws_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(pwsVel_pws_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(pwsVel_pws_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
pwsVel_pws_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inter~ 116. 32.1 3.62 107. 4.47e-4 52.6
## 2 fixed <NA> stride~ -44.4 14.5 -3.06 97.7 2.85e-3 -73.2
## 3 fixed <NA> log_de~ 2.13 3.59 0.595 119. 5.53e-1 -4.97
## 4 fixed <NA> mean_c~ 0.564 0.172 3.28 111. 1.38e-3 0.223
## 5 fixed <NA> stride~ -1.48 0.545 -2.72 157. 7.25e-3 -2.56
## 6 ran_pars bw_id sd__(I~ 16.2 NA NA NA NA NA
## 7 ran_pars Residual sd__Ob~ 14.9 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
pwsVel_pws_multi_vid_adj <- lmer(PWS_velocitycmsecmean ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_pws_clean_df)
summary(pwsVel_pws_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PWS_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_pws_clean_df
##
## REML criterion at convergence: 1361
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91825 -0.50698 0.06095 0.56417 1.47833
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 224.0 14.97
## Residual 213.6 14.61
## Number of obs: 162, groups: bw_id, 127
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 100.1378 33.6497 113.7658
## stride_time_median_sec_pose_zv -34.4029 14.4747 99.5882
## log_delta_pix_h_rel_median_pose_zv 0.2555 3.5953 114.5385
## mean_cadence_step_per_min_pose_zv 0.6566 0.1711 107.7974
## stride_width_median_cm_pose_zv -0.7604 0.5647 146.9866
## demoEHR_Age -0.2565 0.1854 108.9989
## demoEHR_DiseaseDuration -0.1670 0.2648 101.8196
## ms_dx_condensedProgressive MS -13.5472 5.9343 116.3875
## ms_dx_condensedMS, Subtype Not Specified 14.1187 15.3541 125.8439
## race_ethnicity_cleanAsian -1.9507 7.8698 109.4660
## race_ethnicity_cleanBlack Or African American -22.2538 7.1440 99.6541
## race_ethnicity_cleanHispanic or Latino 1.6386 6.2964 102.2530
## race_ethnicity_cleanOther/Unknown/Declined -0.1223 8.0998 115.0832
## clean_sexMale 4.9404 4.2930 109.6597
## clean_sexNon-Binary 1.0418 21.2748 126.2811
## t value Pr(>|t|)
## (Intercept) 2.976 0.00357 **
## stride_time_median_sec_pose_zv -2.377 0.01938 *
## log_delta_pix_h_rel_median_pose_zv 0.071 0.94347
## mean_cadence_step_per_min_pose_zv 3.837 0.00021 ***
## stride_width_median_cm_pose_zv -1.347 0.18020
## demoEHR_Age -1.384 0.16925
## demoEHR_DiseaseDuration -0.631 0.52955
## ms_dx_condensedProgressive MS -2.283 0.02425 *
## ms_dx_condensedMS, Subtype Not Specified 0.920 0.35957
## race_ethnicity_cleanAsian -0.248 0.80470
## race_ethnicity_cleanBlack Or African American -3.115 0.00240 **
## race_ethnicity_cleanHispanic or Latino 0.260 0.79519
## race_ethnicity_cleanOther/Unknown/Declined -0.015 0.98798
## clean_sexMale 1.151 0.25231
## clean_sexNon-Binary 0.049 0.96102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Residuals vs. fitted values
hist(resid(pwsVel_pws_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(pwsVel_pws_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
pwsVel_pws_multi_vid_adj_df <- tidy(pwsVel_pws_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(pwsVel_pws_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(pwsVel_pws_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")
pwsVel_pws_multi_vid_adj_df## # A tibble: 17 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 100. 33.6 2.98 114. 3.57e-3 33.5
## 2 fixed <NA> strid~ -34.4 14.5 -2.38 99.6 1.94e-2 -63.1
## 3 fixed <NA> log_d~ 0.255 3.60 0.0711 115. 9.43e-1 -6.87
## 4 fixed <NA> mean_~ 0.657 0.171 3.84 108. 2.10e-4 0.317
## 5 fixed <NA> strid~ -0.760 0.565 -1.35 147. 1.80e-1 -1.88
## 6 fixed <NA> demoE~ -0.257 0.185 -1.38 109. 1.69e-1 -0.624
## 7 fixed <NA> demoE~ -0.167 0.265 -0.631 102. 5.30e-1 -0.692
## 8 fixed <NA> ms_dx~ -13.5 5.93 -2.28 116. 2.43e-2 -25.3
## 9 fixed <NA> ms_dx~ 14.1 15.4 0.920 126. 3.60e-1 -16.3
## 10 fixed <NA> race_~ -1.95 7.87 -0.248 109. 8.05e-1 -17.5
## 11 fixed <NA> race_~ -22.3 7.14 -3.12 99.7 2.40e-3 -36.4
## 12 fixed <NA> race_~ 1.64 6.30 0.260 102. 7.95e-1 -10.8
## 13 fixed <NA> race_~ -0.122 8.10 -0.0151 115. 9.88e-1 -16.2
## 14 fixed <NA> clean~ 4.94 4.29 1.15 110. 2.52e-1 -3.57
## 15 fixed <NA> clean~ 1.04 21.3 0.0490 126. 9.61e-1 -41.1
## 16 ran_pars bw_id sd__(~ 15.0 NA NA NA NA NA
## 17 ran_pars Residual sd__O~ 14.6 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>ANOVA - Compare Multivar Models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(pwsVel_pws_multi_dem, pwsVel_pws_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_pws_clean_df
## Models:
## pwsVel_pws_multi_dem: PWS_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## pwsVel_pws_multi_vid_adj: PWS_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## pwsVel_pws_multi_dem 13 1493.9 1534.0 -733.96 1467.9
## pwsVel_pws_multi_vid_adj 17 1452.3 1504.8 -709.16 1418.3 49.596 4
## Pr(>Chisq)
## pwsVel_pws_multi_dem
## pwsVel_pws_multi_vid_adj 4.386e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Video only vs Video + Demographics
anova(pwsVel_pws_multi_vid_unadj, pwsVel_pws_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_pws_clean_df
## Models:
## pwsVel_pws_multi_vid_unadj: PWS_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + (1 | bw_id)
## pwsVel_pws_multi_vid_adj: PWS_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## pwsVel_pws_multi_vid_unadj 7 1458.3 1479.9 -722.14 1444.3
## pwsVel_pws_multi_vid_adj 17 1452.3 1504.8 -709.16 1418.3 25.97 10
## Pr(>Chisq)
## pwsVel_pws_multi_vid_unadj
## pwsVel_pws_multi_vid_adj 0.003781 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
pwsVel_pws_multivar_all <- bind_rows(pwsVel_pws_multi_dem_df,
pwsVel_pws_multi_vid_unadj_df,
pwsVel_pws_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- pwsVel_pws_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.76 0.21
## 2 Unadjusted - Multivariate Video Metrics 0.69 0.32
## 3 Adjusted - Multivariate Video Metrics 0.72 0.42write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelPws_adj_vs_unadj_pws_r2.csv'))Plot Unadjusted vs Adjusted Estimates
p <- adj_vs_unadj_plot(results_df = pwsVel_pws_multivar_all,
plot_title = "PWS: PWS Mat Velocity Unadjusted vs Adjusted",
x_adj = 30)
p
ggsave(file.path(output_dir, 'ZenoVelPws_adj_vs_unadj_pws_estimates.png'),
bg = "white", width= 12, height=10)Fast Walking speed videos
Univariate Models
uni_zv_fw_fwvel <- all_univariate_mixed_effect(data = zeno_fw_df,
outcome_column = "FW_velocitycmsecmean")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 4 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ log_delta_pix_h_rel_median_pose + (1 |
## bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: 2123.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8984 -0.4910 -0.0405 0.5122 3.8087
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 409.0 20.22
## Residual 686.3 26.20
## Number of obs: 218, groups: bw_id, 152
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 198.547 4.986 209.813 39.82 <2e-16
## log_delta_pix_h_rel_median_pose 47.532 4.066 213.984 11.69 <2e-16
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.870
## [1] "stride_time_median_sec_pose"## Warning: Removed 53 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: 1642
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.63128 -0.42916 0.00867 0.45414 2.95998
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 504.3 22.46
## Residual 609.9 24.70
## Number of obs: 169, groups: bw_id, 126
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 288.84 15.04 155.63 19.207 <2e-16 ***
## stride_time_median_sec_pose -147.45 15.53 159.51 -9.492 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.982
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 46 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ mean_cadence_step_per_min_pose + (1 |
## bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: 1757.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18927 -0.57963 0.01518 0.54555 2.08844
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 488.6 22.10
## Residual 859.3 29.31
## Number of obs: 176, groups: bw_id, 129
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 22.8532 15.4932 170.9643 1.475 0.142
## mean_cadence_step_per_min_pose 1.0376 0.1273 170.5877 8.149 7.51e-14
##
## (Intercept)
## mean_cadence_step_per_min_pose ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.981
## [1] "stride_width_median_cm_pose"## Warning: Removed 47 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: 1774.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.50008 -0.27623 0.03442 0.41610 1.94751
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1268.4 35.61
## Residual 579.6 24.07
## Number of obs: 175, groups: bw_id, 128
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 182.2706 12.3191 134.9088 14.796 < 2e-16 ***
## stride_width_median_cm_pose -2.9836 0.9725 121.6592 -3.068 0.00266 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.954
## [1] "demoEHR_Age"
## FW_velocitycmsecmean ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: 2224.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.74601 -0.33528 0.04748 0.37615 2.20638
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1330.9 36.48
## Residual 426.4 20.65
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 208.8291 13.2897 152.4750 15.714 < 2e-16 ***
## demoEHR_Age -1.2409 0.2566 153.5144 -4.837 3.18e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.969
## [1] "demoEHR_DiseaseDuration"
## FW_velocitycmsecmean ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: 2240.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.85661 -0.30254 0.01094 0.39170 2.28142
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1569.3 39.61
## Residual 413.9 20.35
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 155.3424 5.5883 153.1485 27.798 <2e-16 ***
## demoEHR_DiseaseDuration -0.9115 0.4466 154.2343 -2.041 0.043 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.779
## [1] "ms_dx_condensed"
## FW_velocitycmsecmean ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: 2178.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.62728 -0.35557 0.01123 0.42904 2.30067
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1070.6 32.72
## Residual 413.1 20.33
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 158.043 3.354 151.628 47.122
## ms_dx_condensedProgressive MS -60.295 7.737 156.200 -7.793
## ms_dx_condensedMS, Subtype Not Specified -17.697 27.443 173.070 -0.645
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ms_dx_condensedProgressive MS 8.58e-13 ***
## ms_dx_condensedMS, Subtype Not Specified 0.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.433
## ms_d_MS,SNS -0.122 0.053
## [1] "race_ethnicity_clean"
## FW_velocitycmsecmean ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: 2207.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.85257 -0.34419 0.00005 0.38680 2.29436
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1509.2 38.85
## Residual 425.2 20.62
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 148.913 4.182 146.728
## race_ethnicity_cleanAsian -11.056 13.539 144.842
## race_ethnicity_cleanBlack Or African American -36.411 13.697 152.121
## race_ethnicity_cleanHispanic or Latino -2.783 11.889 149.976
## race_ethnicity_cleanOther/Unknown/Declined 15.269 13.047 146.153
## t value Pr(>|t|)
## (Intercept) 35.611 < 2e-16 ***
## race_ethnicity_cleanAsian -0.817 0.41549
## race_ethnicity_cleanBlack Or African American -2.658 0.00869 **
## race_ethnicity_cleanHispanic or Latino -0.234 0.81525
## race_ethnicity_cleanOther/Unknown/Declined 1.170 0.24377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.309
## rc_thn_BOAA -0.305 0.094
## rc_thnc_HoL -0.352 0.109 0.107
## rc_th_O/U/D -0.321 0.099 0.098 0.113
## [1] "clean_sex"
## FW_velocitycmsecmean ~ clean_sex + (1 | bw_id)
## <environment: 0x00000000293ff930>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: 2227.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8099 -0.3291 0.0184 0.3878 2.3219
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1583.8 39.80
## Residual 423.3 20.57
## Number of obs: 222, groups: bw_id, 154
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 143.872 4.077 149.599 35.292 <2e-16 ***
## clean_sexMale 9.645 8.196 150.844 1.177 0.241
## clean_sexNon-Binary 32.050 42.568 131.966 0.753 0.453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.497
## cln_sxNn-Bn -0.096 0.048
# save adjusted r_squared
adj_r_squared_df <- uni_zv_fw_fwvel[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.61 0.38
## 2 Stride Time - Median 0.65 0.35
## 3 Cadence - Mean 0.54 0.28
## 4 Stride Width - Median 0.7 0.04
## 5 Age 0.79 0.12
## 6 Disease Duration 0.8 0.03
## 7 MS DX 0.79 0.26
## 8 Race and Ethnicity 0.79 0.05
## 9 Sex 0.79 0.01write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelFw_univariate_fw_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_zv_fw_fwvel,
plot_title = "FW Univariate - lmer(Zeno Mat Velocity ~ Predictor + (1 | bw_id))",
x_adj = 15)
p 
ggsave(file.path(output_dir, 'ZenoVelFw_univariate_fw_estimates.png'),
bg = "white", width= 10, height=10)Multivariate Demographics and MS Info
fwVel_fw_multi_dem <- lmer(FW_velocitycmsecmean ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_fw_clean_df)
summary(fwVel_fw_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FW_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration +
## ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_fw_clean_df
##
## REML criterion at convergence: 1571.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22452 -0.40797 0.02676 0.40013 2.21919
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 891.0 29.85
## Residual 400.1 20.00
## Number of obs: 167, groups: bw_id, 125
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 206.8403 15.5531 119.7069
## demoEHR_Age -0.9585 0.3086 122.3187
## demoEHR_DiseaseDuration 0.2485 0.4601 115.5376
## ms_dx_condensedProgressive MS -49.3331 9.1143 119.4695
## ms_dx_condensedMS, Subtype Not Specified -16.7360 37.4419 127.5203
## race_ethnicity_cleanAsian -22.3427 12.2269 116.4010
## race_ethnicity_cleanBlack Or African American -51.8062 12.3392 114.3895
## race_ethnicity_cleanHispanic or Latino -15.6399 11.1026 115.1932
## race_ethnicity_cleanOther/Unknown/Declined -5.4296 12.6180 118.6096
## clean_sexMale 7.1075 7.4584 115.1542
## clean_sexNon-Binary 13.1729 33.6699 92.5670
## t value Pr(>|t|)
## (Intercept) 13.299 < 2e-16 ***
## demoEHR_Age -3.106 0.00236 **
## demoEHR_DiseaseDuration 0.540 0.59020
## ms_dx_condensedProgressive MS -5.413 3.25e-07 ***
## ms_dx_condensedMS, Subtype Not Specified -0.447 0.65564
## race_ethnicity_cleanAsian -1.827 0.07021 .
## race_ethnicity_cleanBlack Or African American -4.198 5.34e-05 ***
## race_ethnicity_cleanHispanic or Latino -1.409 0.16162
## race_ethnicity_cleanOther/Unknown/Declined -0.430 0.66775
## clean_sexMale 0.953 0.34261
## clean_sexNon-Binary 0.391 0.69652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__OAA r__HoL r__O/U
## demoEHR_Age -0.908
## dmEHR_DssDr 0.095 -0.404
## ms_dx_cnPMS 0.154 -0.215 -0.102
## ms_d_MS,SNS 0.097 -0.053 -0.166 0.095
## rc_thncty_A -0.339 0.235 0.040 -0.016 -0.003
## rc_thn_BOAA -0.181 0.086 0.015 0.029 0.015 0.130
## rc_thnc_HoL -0.276 0.203 -0.026 -0.042 0.029 0.170 0.127
## rc_th_O/U/D -0.381 0.255 0.025 0.043 -0.027 0.181 0.137 0.151
## clean_sexMl -0.219 0.083 0.031 -0.026 -0.165 0.003 0.039 -0.089 0.181
## cln_sxNn-Bn -0.079 -0.002 0.127 0.020 -0.022 0.058 0.047 0.042 0.069
## cln_sM
## demoEHR_Age
## dmEHR_DssDr
## ms_dx_cnPMS
## ms_d_MS,SNS
## rc_thncty_A
## rc_thn_BOAA
## rc_thnc_HoL
## rc_th_O/U/D
## clean_sexMl
## cln_sxNn-Bn 0.066# Residuals vs. fitted values
hist(resid(fwVel_fw_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(fwVel_fw_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
fwVel_fw_multi_dem_df <- tidy(fwVel_fw_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(fwVel_fw_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(fwVel_fw_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
fwVel_fw_multi_dem_df## # A tibble: 13 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Int~ 207. 15.6 13.3 120. 2.45e-25 176.
## 2 fixed <NA> demo~ -0.959 0.309 -3.11 122. 2.36e- 3 -1.57
## 3 fixed <NA> demo~ 0.248 0.460 0.540 116. 5.90e- 1 -0.663
## 4 fixed <NA> ms_d~ -49.3 9.11 -5.41 119. 3.25e- 7 -67.4
## 5 fixed <NA> ms_d~ -16.7 37.4 -0.447 128. 6.56e- 1 -90.8
## 6 fixed <NA> race~ -22.3 12.2 -1.83 116. 7.02e- 2 -46.6
## 7 fixed <NA> race~ -51.8 12.3 -4.20 114. 5.34e- 5 -76.2
## 8 fixed <NA> race~ -15.6 11.1 -1.41 115. 1.62e- 1 -37.6
## 9 fixed <NA> race~ -5.43 12.6 -0.430 119. 6.68e- 1 -30.4
## 10 fixed <NA> clea~ 7.11 7.46 0.953 115. 3.43e- 1 -7.67
## 11 fixed <NA> clea~ 13.2 33.7 0.391 92.6 6.97e- 1 -53.7
## 12 ran_pars bw_id sd__~ 29.8 NA NA NA NA NA
## 13 ran_pars Residual sd__~ 20.0 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
fwVel_fw_multi_vid_unadj <- lmer(FW_velocitycmsecmean ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
(1|bw_id),
data = zeno_fw_clean_df)
summary(fwVel_fw_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FW_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## (1 | bw_id)
## Data: zeno_fw_clean_df
##
## REML criterion at convergence: 1566.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.66419 -0.56234 -0.03055 0.60676 2.70777
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 70.41 8.391
## Residual 712.69 26.696
## Number of obs: 167, groups: bw_id, 125
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 252.3937 36.9259 161.9680 6.835
## stride_time_median_sec_pose_zv -80.0099 20.0418 161.9663 -3.992
## log_delta_pix_h_rel_median_pose_zv 31.9134 4.9230 159.9963 6.483
## mean_cadence_step_per_min_pose_zv 0.2693 0.1695 161.9959 1.589
## stride_width_median_cm_pose_zv -2.1013 0.8291 160.2338 -2.534
## Pr(>|t|)
## (Intercept) 1.58e-10 ***
## stride_time_median_sec_pose_zv 9.91e-05 ***
## log_delta_pix_h_rel_median_pose_zv 1.07e-09 ***
## mean_cadence_step_per_min_pose_zv 0.1141
## stride_width_median_cm_pose_zv 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.829
## lg_dl______ 0.054 0.237
## mn_cdn_____ -0.915 0.660 -0.170
## strd_wd____ -0.189 -0.074 0.220 -0.026# Residuals vs. fitted values
hist(resid(fwVel_fw_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(fwVel_fw_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
fwVel_fw_multi_vid_unadj_df <- tidy(fwVel_fw_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(fwVel_fw_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(fwVel_fw_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
fwVel_fw_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 252. 36.9 6.84 162. 1.58e-10 1.79e+2
## 2 fixed <NA> strid~ -80.0 20.0 -3.99 162. 9.91e- 5 -1.20e+2
## 3 fixed <NA> log_d~ 31.9 4.92 6.48 160. 1.07e- 9 2.22e+1
## 4 fixed <NA> mean_~ 0.269 0.170 1.59 162. 1.14e- 1 -6.55e-2
## 5 fixed <NA> strid~ -2.10 0.829 -2.53 160. 1.22e- 2 -3.74e+0
## 6 ran_pars bw_id sd__(~ 8.39 NA NA NA NA NA
## 7 ran_pars Residual sd__O~ 26.7 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
fwVel_fw_multi_vid_adj <- lmer(FW_velocitycmsecmean ~
stride_time_median_sec_pose_zv +
log_delta_pix_h_rel_median_pose_zv +
mean_cadence_step_per_min_pose_zv +
stride_width_median_cm_pose_zv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = zeno_fw_clean_df)## boundary (singular) fit: see help('isSingular')summary(fwVel_fw_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FW_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_fw_clean_df
##
## REML criterion at convergence: 1470.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3491 -0.5524 0.0191 0.5929 2.5253
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1.757e-13 4.192e-07
## Residual 6.154e+02 2.481e+01
## Number of obs: 167, groups: bw_id, 125
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 279.05497 34.97490 152.00000
## stride_time_median_sec_pose_zv -70.43068 18.30922 152.00000
## log_delta_pix_h_rel_median_pose_zv 26.43804 4.59993 152.00000
## mean_cadence_step_per_min_pose_zv 0.30823 0.15484 152.00000
## stride_width_median_cm_pose_zv -1.77231 0.78927 152.00000
## demoEHR_Age -0.94283 0.20187 152.00000
## demoEHR_DiseaseDuration 0.09378 0.28616 152.00000
## ms_dx_condensedProgressive MS -9.57995 6.43577 152.00000
## ms_dx_condensedMS, Subtype Not Specified -21.09913 25.73336 152.00000
## race_ethnicity_cleanAsian -10.01438 7.78829 152.00000
## race_ethnicity_cleanBlack Or African American -31.79119 7.89517 152.00000
## race_ethnicity_cleanHispanic or Latino -10.52545 7.04757 152.00000
## race_ethnicity_cleanOther/Unknown/Declined -6.10940 8.14599 152.00000
## clean_sexMale 8.29111 4.72665 152.00000
## clean_sexNon-Binary 9.88749 18.06623 152.00000
## t value Pr(>|t|)
## (Intercept) 7.979 3.33e-13 ***
## stride_time_median_sec_pose_zv -3.847 0.000176 ***
## log_delta_pix_h_rel_median_pose_zv 5.747 4.80e-08 ***
## mean_cadence_step_per_min_pose_zv 1.991 0.048323 *
## stride_width_median_cm_pose_zv -2.246 0.026178 *
## demoEHR_Age -4.671 6.56e-06 ***
## demoEHR_DiseaseDuration 0.328 0.743569
## ms_dx_condensedProgressive MS -1.489 0.138678
## ms_dx_condensedMS, Subtype Not Specified -0.820 0.413549
## race_ethnicity_cleanAsian -1.286 0.200459
## race_ethnicity_cleanBlack Or African American -4.027 8.90e-05 ***
## race_ethnicity_cleanHispanic or Latino -1.493 0.137383
## race_ethnicity_cleanOther/Unknown/Declined -0.750 0.454421
## clean_sexMale 1.754 0.081426 .
## clean_sexNon-Binary 0.547 0.584981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# Residuals vs. fitted values
hist(resid(fwVel_fw_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(fwVel_fw_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
fwVel_fw_multi_vid_adj_df <- tidy(fwVel_fw_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(fwVel_fw_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(fwVel_fw_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")## Random effect variances not available. Returned R2 does not account for random effects.## Warning: There was 1 warning in `mutate()`.
## i In argument: `R2_conditional = r2(fwVel_fw_multi_vid_adj)$R2_conditional`.
## Caused by warning:
## ! Can't compute random effect variances. Some variance components equal
## zero. Your model may suffer from singularity (see `?lme4::isSingular`
## and `?performance::check_singularity`).
## Solution: Respecify random structure! You may also decrease the
## `tolerance` level to enforce the calculation of random effect variances.## Random effect variances not available. Returned R2 does not account for random effects.## Warning: There was 1 warning in `mutate()`.
## i In argument: `R2_marginal = r2(fwVel_fw_multi_vid_adj)$R2_marginal`.
## Caused by warning:
## ! Can't compute random effect variances. Some variance components equal
## zero. Your model may suffer from singularity (see `?lme4::isSingular`
## and `?performance::check_singularity`).
## Solution: Respecify random structure! You may also decrease the
## `tolerance` level to enforce the calculation of random effect variances.fwVel_fw_multi_vid_adj## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula:
## FW_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv +
## mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: zeno_fw_clean_df
## REML criterion at convergence: 1470.291
## Random effects:
## Groups Name Std.Dev.
## bw_id (Intercept) 4.192e-07
## Residual 2.481e+01
## Number of obs: 167, groups: bw_id, 125
## Fixed Effects:
## (Intercept)
## 279.05497
## stride_time_median_sec_pose_zv
## -70.43068
## log_delta_pix_h_rel_median_pose_zv
## 26.43804
## mean_cadence_step_per_min_pose_zv
## 0.30823
## stride_width_median_cm_pose_zv
## -1.77231
## demoEHR_Age
## -0.94283
## demoEHR_DiseaseDuration
## 0.09378
## ms_dx_condensedProgressive MS
## -9.57995
## ms_dx_condensedMS, Subtype Not Specified
## -21.09913
## race_ethnicity_cleanAsian
## -10.01438
## race_ethnicity_cleanBlack Or African American
## -31.79119
## race_ethnicity_cleanHispanic or Latino
## -10.52545
## race_ethnicity_cleanOther/Unknown/Declined
## -6.10940
## clean_sexMale
## 8.29111
## clean_sexNon-Binary
## 9.88749
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningsANOVA - Compare Multivar Models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(fwVel_fw_multi_dem, fwVel_fw_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_fw_clean_df
## Models:
## fwVel_fw_multi_dem: FW_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## fwVel_fw_multi_vid_adj: FW_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## fwVel_fw_multi_dem 13 1657.2 1697.7 -815.57 1631.2
## fwVel_fw_multi_vid_adj 17 1564.7 1617.8 -765.37 1530.7 100.41 4 < 2.2e-16
##
## fwVel_fw_multi_dem
## fwVel_fw_multi_vid_adj ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Video only vs Video + Demographics
anova(fwVel_fw_multi_vid_unadj, fwVel_fw_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: zeno_fw_clean_df
## Models:
## fwVel_fw_multi_vid_unadj: FW_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + (1 | bw_id)
## fwVel_fw_multi_vid_adj: FW_velocitycmsecmean ~ stride_time_median_sec_pose_zv + log_delta_pix_h_rel_median_pose_zv + mean_cadence_step_per_min_pose_zv + stride_width_median_cm_pose_zv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## fwVel_fw_multi_vid_unadj 7 1595.3 1617.1 -790.63 1581.3
## fwVel_fw_multi_vid_adj 17 1564.7 1617.8 -765.37 1530.7 50.518 10
## Pr(>Chisq)
## fwVel_fw_multi_vid_unadj
## fwVel_fw_multi_vid_adj 2.143e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
fwVel_fw_multivar_all <- bind_rows(fwVel_fw_multi_dem_df,
fwVel_fw_multi_vid_unadj_df,
fwVel_fw_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- fwVel_fw_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.8 0.34
## 2 Unadjusted - Multivariate Video Metrics 0.61 0.57
## 3 Adjusted - Multivariate Video Metrics NA 0.68write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelFw_adj_vs_unadj_fw_r2.csv'))Plot Unadjusted vs Adjusted Estimates
p <- adj_vs_unadj_plot(results_df = fwVel_fw_multivar_all,
plot_title = "FW: FW Mat Velocity Unadjusted vs Adjusted",
x_adj = 30)
p
ggsave(file.path(output_dir, 'ZenoVelFw_adj_vs_unadj_fw_estimates.png'),
bg = "white", width= 12, height=10)Home Videos: FW Velocity Outcome
Univariate Models
uni_home_fwvel <- all_univariate_mixed_effect(data = home_df,
outcome_column = "FW_velocitycmsecmean")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 4 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ log_delta_pix_h_rel_median_pose + (1 |
## bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: 513.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.69467 -0.26905 -0.00283 0.19775 1.73388
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1638.07 40.473
## Residual 36.76 6.063
## Number of obs: 61, groups: bw_id, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 151.176 9.180 51.080 16.468 <2e-16
## log_delta_pix_h_rel_median_pose 8.717 3.914 34.971 2.227 0.0325
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.586
## [1] "stride_time_median_sec_pose"## Warning: Removed 13 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: 431.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.80805 -0.10652 -0.01622 0.11369 1.82623
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1784.65 42.245
## Residual 43.83 6.621
## Number of obs: 52, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 151.69 22.01 48.32 6.891 1.05e-08 ***
## stride_time_median_sec_pose -8.74 16.87 41.05 -0.518 0.607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.915
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 12 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ mean_cadence_step_per_min_pose + (1 |
## bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: 448.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.87981 -0.10562 -0.00789 0.07971 1.86691
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1851.12 43.025
## Residual 41.38 6.433
## Number of obs: 53, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 138.70843 12.43930 48.79250 11.151 5.08e-15
## mean_cadence_step_per_min_pose 0.02461 0.08320 30.02432 0.296 0.769
##
## (Intercept) ***
## mean_cadence_step_per_min_pose
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.689
## [1] "stride_width_median_cm_pose"## Warning: Removed 12 rows containing missing values (`geom_point()`).
## FW_velocitycmsecmean ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: 443.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.89119 -0.15311 0.03553 0.14762 1.88743
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1778.41 42.171
## Residual 41.23 6.421
## Number of obs: 53, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 149.5893 11.9259 47.4357 12.543 <2e-16 ***
## stride_width_median_cm_pose -0.6319 0.6059 30.3933 -1.043 0.305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.671
## [1] "demoEHR_Age"
## FW_velocitycmsecmean ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: 541.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95618 -0.05899 0.00418 0.06125 1.99974
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1328.89 36.454
## Residual 35.14 5.928
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 224.3744 28.0855 28.9988 7.989 8.24e-09 ***
## demoEHR_Age -1.6390 0.5244 29.0037 -3.126 0.00401 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.972
## [1] "demoEHR_DiseaseDuration"
## FW_velocitycmsecmean ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: 548.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.92446 -0.08034 -0.00509 0.06452 1.95181
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1662.1 40.77
## Residual 36.6 6.05
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 147.4952 12.4244 29.5997 11.871 8.82e-13 ***
## demoEHR_DiseaseDuration -0.7637 0.9039 30.5298 -0.845 0.405
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.805
## [1] "ms_dx_condensed"
## FW_velocitycmsecmean ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: 523.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98075 -0.08826 -0.01422 0.06047 1.97454
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1244.55 35.278
## Residual 35.15 5.929
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 149.957 7.256 28.051 20.667
## ms_dx_condensedProgressive MS -63.876 17.486 28.127 -3.653
## ms_dx_condensedMS, Subtype Not Specified -9.611 26.148 27.995 -0.368
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ms_dx_condensedProgressive MS 0.00105 **
## ms_dx_condensedMS, Subtype Not Specified 0.71597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.415
## ms_d_MS,SNS -0.277 0.115
## [1] "race_ethnicity_clean"
## FW_velocitycmsecmean ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: 506.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96698 -0.07540 0.00000 0.04761 1.98937
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1418.07 37.657
## Residual 35.13 5.927
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 136.55 7.74 26.11
## race_ethnicity_cleanAsian 15.71 27.89 26.04
## race_ethnicity_cleanBlack Or African American -84.10 38.67 26.03
## race_ethnicity_cleanHispanic or Latino -17.21 38.67 26.03
## race_ethnicity_cleanOther/Unknown/Declined 49.05 23.20 26.03
## t value Pr(>|t|)
## (Intercept) 17.642 5e-16 ***
## race_ethnicity_cleanAsian 0.563 0.5780
## race_ethnicity_cleanBlack Or African American -2.175 0.0389 *
## race_ethnicity_cleanHispanic or Latino -0.445 0.6601
## race_ethnicity_cleanOther/Unknown/Declined 2.114 0.0443 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.278
## rc_thn_BOAA -0.200 0.056
## rc_thnc_HoL -0.200 0.056 0.040
## rc_th_O/U/D -0.334 0.093 0.067 0.067
## [1] "clean_sex"
## FW_velocitycmsecmean ~ clean_sex + (1 | bw_id)
## <environment: 0x000000002f8babd8>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: 532
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96730 -0.09426 0.00063 0.04308 1.98812
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1744.09 41.762
## Residual 35.15 5.929
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 135.020 8.571 28.059 15.753 1.83e-15 ***
## clean_sexMale 12.553 19.172 28.096 0.655 0.518
## clean_sexNon-Binary 49.291 42.838 28.016 1.151 0.260
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.447
## cln_sxNn-Bn -0.200 0.089
# save adjusted r_squared
adj_r_squared_df <- uni_home_fwvel[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.98 0.01
## 2 Stride Time - Median 0.98 0
## 3 Cadence - Mean 0.98 0
## 4 Stride Width - Median 0.98 0
## 5 Age 0.98 0.25
## 6 Disease Duration 0.98 0.02
## 7 MS DX 0.98 0.28
## 8 Race and Ethnicity 0.98 0.28
## 9 Sex 0.98 0.05write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelFW_univariate_home_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_home_fwvel,
plot_title = "Home Univariate - lmer(Zeno Mat FW Velocity ~ Predictor + (1 | bw_id))",
x_adj = 15)
p 
ggsave(file.path(output_dir, 'ZenoVelFW_univariate_home_estimates.png'),
bg = "white", width= 10, height=10)Multivariate Demographics and MS Info
fwVel_home_multi_dem <- lmer(FW_velocitycmsecmean ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = home_clean_df)
summary(fwVel_home_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FW_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration +
## ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: 356.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83460 -0.11605 0.00000 0.08546 1.83106
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 875.89 29.595
## Residual 40.92 6.397
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 210.6935 34.9829 13.1121 6.023
## demoEHR_Age -1.3525 0.6764 13.3789 -1.999
## demoEHR_DiseaseDuration 0.8707 0.9850 14.5492 0.884
## ms_dx_condensedProgressive MS -66.7269 17.8094 13.0963 -3.747
## ms_dx_condensedMS, Subtype Not Specified -0.7685 25.8295 13.1139 -0.030
## race_ethnicity_cleanAsian -7.5722 33.7486 13.0566 -0.224
## race_ethnicity_cleanHispanic or Latino -22.0200 34.8463 13.2505 -0.632
## race_ethnicity_cleanOther/Unknown/Declined 20.4081 22.3264 13.2381 0.914
## clean_sexMale 12.0822 17.7360 13.3034 0.681
## Pr(>|t|)
## (Intercept) 4.13e-05 ***
## demoEHR_Age 0.06629 .
## demoEHR_DiseaseDuration 0.39111
## ms_dx_condensedProgressive MS 0.00241 **
## ms_dx_condensedMS, Subtype Not Specified 0.97671
## race_ethnicity_cleanAsian 0.82594
## race_ethnicity_cleanHispanic or Latino 0.53819
## race_ethnicity_cleanOther/Unknown/Declined 0.37702
## clean_sexMale 0.50741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__HoL r__O/U
## demoEHR_Age -0.936
## dmEHR_DssDr 0.234 -0.460
## ms_dx_cnPMS 0.088 -0.186 -0.049
## ms_d_MS,SNS 0.307 -0.329 -0.105 0.268
## rc_thncty_A -0.426 0.343 -0.046 0.042 -0.082
## rc_thnc_HoL 0.060 -0.042 -0.363 0.193 0.203 0.009
## rc_th_O/U/D -0.521 0.457 -0.300 0.118 -0.019 0.264 0.141
## clean_sexMl -0.486 0.423 -0.249 -0.071 -0.263 0.244 0.112 0.361# Residuals vs. fitted values
hist(resid(fwVel_home_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(fwVel_home_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
fwVel_home_multi_dem_df <- tidy(fwVel_home_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(fwVel_home_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(fwVel_home_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
fwVel_home_multi_dem_df## # A tibble: 11 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 211. 35.0 6.02 13.1 4.13e-5 135.
## 2 fixed <NA> demoE~ -1.35 0.676 -2.00 13.4 6.63e-2 -2.81
## 3 fixed <NA> demoE~ 0.871 0.985 0.884 14.5 3.91e-1 -1.23
## 4 fixed <NA> ms_dx~ -66.7 17.8 -3.75 13.1 2.41e-3 -105.
## 5 fixed <NA> ms_dx~ -0.768 25.8 -0.0298 13.1 9.77e-1 -56.5
## 6 fixed <NA> race_~ -7.57 33.7 -0.224 13.1 8.26e-1 -80.4
## 7 fixed <NA> race_~ -22.0 34.8 -0.632 13.3 5.38e-1 -97.2
## 8 fixed <NA> race_~ 20.4 22.3 0.914 13.2 3.77e-1 -27.7
## 9 fixed <NA> clean~ 12.1 17.7 0.681 13.3 5.07e-1 -26.1
## 10 ran_pars bw_id sd__(~ 29.6 NA NA NA NA NA
## 11 ran_pars Residual sd__O~ 6.40 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
fwVel_home_multi_vid_unadj <- lmer(FW_velocitycmsecmean ~
stride_time_median_sec_pose_hv +
log_delta_pix_h_rel_median_pose_hv +
mean_cadence_step_per_min_pose_hv +
stride_width_median_cm_pose_hv +
(1|bw_id),
data = home_clean_df)
summary(fwVel_home_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FW_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv +
## mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv +
## (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: 415.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.53831 -0.28556 0.00307 0.24098 1.50813
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 1614.0 40.174
## Residual 45.4 6.738
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 159.78419 29.83331 35.11191 5.356
## stride_time_median_sec_pose_hv -4.02905 18.47811 35.67398 -0.218
## log_delta_pix_h_rel_median_pose_hv 11.59564 6.30694 22.54462 1.839
## mean_cadence_step_per_min_pose_hv 0.01207 0.10825 21.27280 0.112
## stride_width_median_cm_pose_hv -0.07531 0.82928 24.48777 -0.091
## Pr(>|t|)
## (Intercept) 5.41e-06 ***
## stride_time_median_sec_pose_hv 0.8286
## log_delta_pix_h_rel_median_pose_hv 0.0792 .
## mean_cadence_step_per_min_pose_hv 0.9123
## stride_width_median_cm_pose_hv 0.9284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.836
## lg_dl______ -0.124 0.295
## mn_cdn_____ -0.494 0.173 -0.122
## strd_wd____ -0.395 0.287 0.559 -0.120# Residuals vs. fitted values
hist(resid(fwVel_home_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(fwVel_home_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
fwVel_home_multi_vid_unadj_df <- tidy(fwVel_home_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(fwVel_home_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(fwVel_home_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
fwVel_home_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inter~ 160. 29.8 5.36 35.1 5.41e-6 99.2
## 2 fixed <NA> stride~ -4.03 18.5 -0.218 35.7 8.29e-1 -41.5
## 3 fixed <NA> log_de~ 11.6 6.31 1.84 22.5 7.92e-2 -1.47
## 4 fixed <NA> mean_c~ 0.0121 0.108 0.112 21.3 9.12e-1 -0.213
## 5 fixed <NA> stride~ -0.0753 0.829 -0.0908 24.5 9.28e-1 -1.79
## 6 ran_pars bw_id sd__(I~ 40.2 NA NA NA NA NA
## 7 ran_pars Residual sd__Ob~ 6.74 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
fwVel_home_multi_vid_adj <- lmer(FW_velocitycmsecmean ~
stride_time_median_sec_pose_hv +
log_delta_pix_h_rel_median_pose_hv +
mean_cadence_step_per_min_pose_hv +
stride_width_median_cm_pose_hv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = home_clean_df)
summary(fwVel_home_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FW_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv +
## mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: 340.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.63090 -0.15114 -0.04039 0.21184 1.70177
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 890.07 29.83
## Residual 38.81 6.23
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 193.12178 46.32771 27.41588
## stride_time_median_sec_pose_hv 18.31244 18.25904 30.87315
## log_delta_pix_h_rel_median_pose_hv 12.27164 5.83158 25.57319
## mean_cadence_step_per_min_pose_hv -0.01109 0.09981 24.71756
## stride_width_median_cm_pose_hv 0.43190 0.77810 26.33512
## demoEHR_Age -1.22323 0.68757 12.97064
## demoEHR_DiseaseDuration 0.90338 0.99499 14.14396
## ms_dx_condensedProgressive MS -71.54933 20.73602 19.76000
## ms_dx_condensedMS, Subtype Not Specified -7.69715 26.54468 13.28993
## race_ethnicity_cleanAsian -4.10647 34.09676 12.44455
## race_ethnicity_cleanHispanic or Latino -26.48146 35.25317 12.71388
## race_ethnicity_cleanOther/Unknown/Declined 22.11582 22.52235 12.55853
## clean_sexMale 11.91272 18.08882 13.10556
## t value Pr(>|t|)
## (Intercept) 4.169 0.000276 ***
## stride_time_median_sec_pose_hv 1.003 0.323694
## log_delta_pix_h_rel_median_pose_hv 2.104 0.045337 *
## mean_cadence_step_per_min_pose_hv -0.111 0.912449
## stride_width_median_cm_pose_hv 0.555 0.583528
## demoEHR_Age -1.779 0.098659 .
## demoEHR_DiseaseDuration 0.908 0.379125
## ms_dx_condensedProgressive MS -3.450 0.002563 **
## ms_dx_condensedMS, Subtype Not Specified -0.290 0.776314
## race_ethnicity_cleanAsian -0.120 0.906061
## race_ethnicity_cleanHispanic or Latino -0.751 0.466222
## race_ethnicity_cleanOther/Unknown/Declined 0.982 0.344664
## clean_sexMale 0.659 0.521575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Residuals vs. fitted values
hist(resid(fwVel_home_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(fwVel_home_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
fwVel_home_multi_vid_adj_df <- tidy(fwVel_home_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(fwVel_home_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(fwVel_home_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")
fwVel_home_multi_vid_adj_df## # A tibble: 15 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 193. 46.3 4.17 27.4 2.76e-4 98.1
## 2 fixed <NA> strid~ 18.3 18.3 1.00 30.9 3.24e-1 -18.9
## 3 fixed <NA> log_d~ 12.3 5.83 2.10 25.6 4.53e-2 0.275
## 4 fixed <NA> mean_~ -0.0111 0.0998 -0.111 24.7 9.12e-1 -0.217
## 5 fixed <NA> strid~ 0.432 0.778 0.555 26.3 5.84e-1 -1.17
## 6 fixed <NA> demoE~ -1.22 0.688 -1.78 13.0 9.87e-2 -2.71
## 7 fixed <NA> demoE~ 0.903 0.995 0.908 14.1 3.79e-1 -1.23
## 8 fixed <NA> ms_dx~ -71.5 20.7 -3.45 19.8 2.56e-3 -115.
## 9 fixed <NA> ms_dx~ -7.70 26.5 -0.290 13.3 7.76e-1 -64.9
## 10 fixed <NA> race_~ -4.11 34.1 -0.120 12.4 9.06e-1 -78.1
## 11 fixed <NA> race_~ -26.5 35.3 -0.751 12.7 4.66e-1 -103.
## 12 fixed <NA> race_~ 22.1 22.5 0.982 12.6 3.45e-1 -26.7
## 13 fixed <NA> clean~ 11.9 18.1 0.659 13.1 5.22e-1 -27.1
## 14 ran_pars bw_id sd__(~ 29.8 NA NA NA NA NA
## 15 ran_pars Residual sd__O~ 6.23 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>ANOVA - Compare Multivar Models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(fwVel_home_multi_dem, fwVel_home_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: home_clean_df
## Models:
## fwVel_home_multi_dem: FW_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## fwVel_home_multi_vid_adj: FW_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## fwVel_home_multi_dem 11 432.16 453.41 -205.08 410.16
## fwVel_home_multi_vid_adj 15 434.51 463.48 -202.25 404.51 5.655 4
## Pr(>Chisq)
## fwVel_home_multi_dem
## fwVel_home_multi_vid_adj 0.2264# Video only vs Video + Demographics
anova(fwVel_home_multi_vid_unadj, fwVel_home_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: home_clean_df
## Models:
## fwVel_home_multi_vid_unadj: FW_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + (1 | bw_id)
## fwVel_home_multi_vid_adj: FW_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## fwVel_home_multi_vid_unadj 7 447.11 460.63 -216.55 433.11
## fwVel_home_multi_vid_adj 15 434.51 463.48 -202.25 404.51 28.602 8
## Pr(>Chisq)
## fwVel_home_multi_vid_unadj
## fwVel_home_multi_vid_adj 0.0003723 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
fwVel_home_multivar_all <- bind_rows(fwVel_home_multi_dem_df,
fwVel_home_multi_vid_unadj_df,
fwVel_home_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- fwVel_home_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.98 0.6
## 2 Unadjusted - Multivariate Video Metrics 0.97 0.02
## 3 Adjusted - Multivariate Video Metrics 0.98 0.6write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoFW_adj_vs_unadj_home_r2.csv'))Plot Unadjusted vs Adjusted Estimates
p <- adj_vs_unadj_plot(results_df = fwVel_home_multivar_all,
plot_title = "Home: FW Mat Velocity Unadjusted vs Adjusted",
x_adj = 30)
p
ggsave(file.path(output_dir, 'ZenoVelFW_adj_vs_unadj_home_estimates.png'),
bg = "white", width= 12, height=10)Home Videos: PWS Velocity Outcome
Univariate Models
uni_home_pwsvel <- all_univariate_mixed_effect(data = home_df,
outcome_column = "PWS_velocitycmsecmean")## [1] "log_delta_pix_h_rel_median_pose"## Warning: Removed 4 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ log_delta_pix_h_rel_median_pose + (1 |
## bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula1
## Data: data
##
## REML criterion at convergence: 496.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90621 -0.16387 0.00884 0.19968 2.10253
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 716.88 26.775
## Residual 45.42 6.739
## Number of obs: 61, groups: bw_id, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 114.284 7.485 57.928 15.268 <2e-16
## log_delta_pix_h_rel_median_pose 7.948 4.072 42.752 1.952 0.0575
##
## (Intercept) ***
## log_delta_pix_h_rel_median_pose .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg_dlt_____ 0.747
## [1] "stride_time_median_sec_pose"## Warning: Removed 13 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ stride_time_median_sec_pose + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula2
## Data: data
##
## REML criterion at convergence: 415.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90371 -0.21074 -0.03452 0.23932 1.74292
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 676.38 26.007
## Residual 51.32 7.164
## Number of obs: 52, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 143.96 18.89 48.36 7.620 7.93e-10 ***
## stride_time_median_sec_pose -33.91 15.13 49.65 -2.241 0.0295 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_tm_m__ -0.956
## [1] "mean_cadence_step_per_min_pose"## Warning: Removed 12 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ mean_cadence_step_per_min_pose + (1 |
## bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula3
## Data: data
##
## REML criterion at convergence: 435.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05653 -0.16339 -0.03195 0.14299 1.90666
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 873.41 29.553
## Residual 46.75 6.838
## Number of obs: 53, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 97.43456 10.93683 49.90227 8.909 6.88e-12
## mean_cadence_step_per_min_pose 0.05897 0.08724 31.74739 0.676 0.504
##
## (Intercept) ***
## mean_cadence_step_per_min_pose
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mn_cdnc____ -0.821
## [1] "stride_width_median_cm_pose"## Warning: Removed 12 rows containing missing values (`geom_point()`).
## PWS_velocitycmsecmean ~ stride_width_median_cm_pose + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula4
## Data: data
##
## REML criterion at convergence: 431.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84421 -0.16481 -0.00374 0.14024 1.93040
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 857.19 29.278
## Residual 47.37 6.883
## Number of obs: 53, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 109.3423 10.4335 50.7638 10.480 2.7e-14 ***
## stride_width_median_cm_pose -0.4419 0.6362 32.8090 -0.695 0.492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## strd_wdt___ -0.805
## [1] "demoEHR_Age"
## PWS_velocitycmsecmean ~ demoEHR_Age + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula5
## Data: data
##
## REML criterion at convergence: 531.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98313 -0.11763 -0.00876 0.11198 1.96658
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 803.57 28.347
## Residual 39.97 6.322
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 125.9571 21.9630 28.9270 5.735 3.33e-06 ***
## demoEHR_Age -0.4478 0.4101 28.9367 -1.092 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## demoEHR_Age -0.972
## [1] "demoEHR_DiseaseDuration"
## PWS_velocitycmsecmean ~ demoEHR_DiseaseDuration + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula6
## Data: data
##
## REML criterion at convergence: 531
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94378 -0.11440 -0.01186 0.11874 1.99432
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 776.52 27.87
## Residual 41.34 6.43
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 109.8340 8.6367 29.2876 12.717 1.88e-13 ***
## demoEHR_DiseaseDuration -0.6491 0.6308 29.5686 -1.029 0.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## dmEHR_DssDr -0.809
## [1] "ms_dx_condensed"
## PWS_velocitycmsecmean ~ ms_dx_condensed + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula7
## Data: data
##
## REML criterion at convergence: 510.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97230 -0.13529 -0.00839 0.13676 1.97131
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 664.14 25.771
## Residual 40.09 6.332
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 107.128 5.345 27.847 20.041
## ms_dx_condensedProgressive MS -34.072 12.892 28.008 -2.643
## ms_dx_condensedMS, Subtype Not Specified 15.441 19.253 27.738 0.802
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ms_dx_condensedProgressive MS 0.0133 *
## ms_dx_condensedMS, Subtype Not Specified 0.4294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) m__PMS
## ms_dx_cnPMS -0.415
## ms_d_MS,SNS -0.278 0.115
## [1] "race_ethnicity_clean"
## PWS_velocitycmsecmean ~ race_ethnicity_clean + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula8
## Data: data
##
## REML criterion at convergence: 494.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9647 -0.1263 0.0000 0.1302 1.9849
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 744.65 27.288
## Residual 39.97 6.322
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 102.090 5.653 26.096
## race_ethnicity_cleanAsian 24.486 20.354 25.951
## race_ethnicity_cleanBlack Or African American -54.617 28.224 25.945
## race_ethnicity_cleanHispanic or Latino -16.830 28.224 25.945
## race_ethnicity_cleanOther/Unknown/Declined 13.212 16.934 25.925
## t value Pr(>|t|)
## (Intercept) 18.058 2.87e-16 ***
## race_ethnicity_cleanAsian 1.203 0.2398
## race_ethnicity_cleanBlack Or African American -1.935 0.0639 .
## race_ethnicity_cleanHispanic or Latino -0.596 0.5561
## race_ethnicity_cleanOther/Unknown/Declined 0.780 0.4423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rc_t_A r__OAA r__HoL
## rc_thncty_A -0.278
## rc_thn_BOAA -0.200 0.056
## rc_thnc_HoL -0.200 0.056 0.040
## rc_th_O/U/D -0.334 0.093 0.067 0.067
## [1] "clean_sex"
## PWS_velocitycmsecmean ~ clean_sex + (1 | bw_id)
## <environment: 0x0000000012cc1278>
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: formula9
## Data: data
##
## REML criterion at convergence: 513.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95533 -0.11451 -0.00159 0.08492 1.99707
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 745.49 27.304
## Residual 39.91 6.318
## Number of obs: 65, groups: bw_id, 31
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 96.934 5.653 28.148 17.147 <2e-16 ***
## clean_sexMale 25.176 12.652 28.249 1.990 0.0564 .
## clean_sexNon-Binary 26.202 28.238 28.046 0.928 0.3614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cln_sM
## clean_sexMl -0.447
## cln_sxNn-Bn -0.200 0.089
# save adjusted r_squared
adj_r_squared_df <- uni_home_pwsvel[c('Variable', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 9 x 3
## Variable R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Log Pixel Height Proxy 0.94 0.02
## 2 Stride Time - Median 0.94 0.09
## 3 Cadence - Mean 0.95 0
## 4 Stride Width - Median 0.95 0
## 5 Age 0.95 0.04
## 6 Disease Duration 0.95 0.03
## 7 MS DX 0.95 0.19
## 8 Race and Ethnicity 0.96 0.17
## 9 Sex 0.96 0.12write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelPWS_univariate_home_r2.csv'))
# Plot estimates
p <- univariate_regression_plot(results = uni_home_pwsvel,
plot_title = "Home Univariate - lmer(Zeno Mat PWS Velocity ~ Predictor + (1 | bw_id))",
x_adj = 15)
p 
ggsave(file.path(output_dir, 'ZenoVelPWS_univariate_home_estimates.png'),
bg = "white", width= 10, height=10)Multivariate Demographics and MS Info
pwsVel_home_multi_dem <- lmer(PWS_velocitycmsecmean ~
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = home_clean_df)
summary(pwsVel_home_multi_dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PWS_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration +
## ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: 352.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7683 -0.1725 0.0000 0.1119 1.8031
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 441.67 21.016
## Residual 48.88 6.992
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 99.0207 25.2296 13.2978 3.925
## demoEHR_Age 0.1069 0.4896 13.5368 0.218
## demoEHR_DiseaseDuration -0.0466 0.7218 14.1702 -0.065
## ms_dx_condensedProgressive MS -51.8764 12.8508 13.3888 -4.037
## ms_dx_condensedMS, Subtype Not Specified 3.8403 18.6430 13.4083 0.206
## race_ethnicity_cleanAsian 35.9656 24.3295 13.3002 1.478
## race_ethnicity_cleanHispanic or Latino -19.8172 25.1813 13.4368 -0.787
## race_ethnicity_cleanOther/Unknown/Declined 12.5805 16.1394 13.4907 0.779
## clean_sexMale 26.6643 12.8470 13.7155 2.076
## Pr(>|t|)
## (Intercept) 0.00167 **
## demoEHR_Age 0.83045
## demoEHR_DiseaseDuration 0.94942
## ms_dx_condensedProgressive MS 0.00133 **
## ms_dx_condensedMS, Subtype Not Specified 0.83989
## race_ethnicity_cleanAsian 0.16262
## race_ethnicity_cleanHispanic or Latino 0.44495
## race_ethnicity_cleanOther/Unknown/Declined 0.44915
## clean_sexMale 0.05725 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dEHR_A dEHR_D m__PMS m__SNS rc_t_A r__HoL r__O/U
## demoEHR_Age -0.936
## dmEHR_DssDr 0.239 -0.467
## ms_dx_cnPMS 0.089 -0.187 -0.048
## ms_d_MS,SNS 0.307 -0.328 -0.104 0.269
## rc_thncty_A -0.425 0.342 -0.047 0.041 -0.082
## rc_thnc_HoL 0.057 -0.037 -0.368 0.194 0.204 0.009
## rc_th_O/U/D -0.522 0.460 -0.311 0.118 -0.017 0.263 0.147
## clean_sexMl -0.487 0.427 -0.257 -0.072 -0.263 0.244 0.115 0.363# Residuals vs. fitted values
hist(resid(pwsVel_home_multi_dem), main = "Residuals All Demographic and MS Info")
# Normality check
qqnorm(residuals(pwsVel_home_multi_dem), main = "Normal Q-Q plot: Residuals All Demographic and MS Info")
# tidy results
pwsVel_home_multi_dem_df <- tidy(pwsVel_home_multi_dem, conf.int = TRUE) %>%
mutate(R2_conditional = r2(pwsVel_home_multi_dem)$R2_conditional) %>%
mutate(R2_marginal = r2(pwsVel_home_multi_dem)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Demographics and MS Info")
pwsVel_home_multi_dem_df## # A tibble: 11 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 99.0 25.2 3.92 13.3 0.00167 44.6
## 2 fixed <NA> demoE~ 0.107 0.490 0.218 13.5 0.830 -0.947
## 3 fixed <NA> demoE~ -0.0466 0.722 -0.0646 14.2 0.949 -1.59
## 4 fixed <NA> ms_dx~ -51.9 12.9 -4.04 13.4 0.00133 -79.6
## 5 fixed <NA> ms_dx~ 3.84 18.6 0.206 13.4 0.840 -36.3
## 6 fixed <NA> race_~ 36.0 24.3 1.48 13.3 0.163 -16.5
## 7 fixed <NA> race_~ -19.8 25.2 -0.787 13.4 0.445 -74.0
## 8 fixed <NA> race_~ 12.6 16.1 0.779 13.5 0.449 -22.2
## 9 fixed <NA> clean~ 26.7 12.8 2.08 13.7 0.0572 -0.944
## 10 ran_pars bw_id sd__(~ 21.0 NA NA NA NA NA
## 11 ran_pars Residual sd__O~ 6.99 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Unadjusted
pwsVel_home_multi_vid_unadj <- lmer(PWS_velocitycmsecmean ~
stride_time_median_sec_pose_hv +
log_delta_pix_h_rel_median_pose_hv +
mean_cadence_step_per_min_pose_hv +
stride_width_median_cm_pose_hv +
(1|bw_id),
data = home_clean_df)
summary(pwsVel_home_multi_vid_unadj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PWS_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv +
## mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv +
## (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: 401.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55618 -0.31475 -0.00102 0.29833 1.66387
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 596.41 24.422
## Residual 59.27 7.699
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 152.00146 28.64681 42.18052 5.306
## stride_time_median_sec_pose_hv -31.52061 16.94354 45.99499 -1.860
## log_delta_pix_h_rel_median_pose_hv 7.55781 6.80844 30.47481 1.110
## mean_cadence_step_per_min_pose_hv 0.02949 0.11984 26.49102 0.246
## stride_width_median_cm_pose_hv -0.36259 0.87149 33.96509 -0.416
## Pr(>|t|)
## (Intercept) 3.88e-06 ***
## stride_time_median_sec_pose_hv 0.0692 .
## log_delta_pix_h_rel_median_pose_hv 0.2757
## mean_cadence_step_per_min_pose_hv 0.8075
## stride_width_median_cm_pose_hv 0.6800
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) strd_t____ l_____ m_____
## strd_tm____ -0.820
## lg_dl______ -0.110 0.306
## mn_cdn_____ -0.623 0.276 -0.079
## strd_wd____ -0.362 0.201 0.528 -0.079# Residuals vs. fitted values
hist(resid(pwsVel_home_multi_vid_unadj), main = "Residuals All Video Metrics Unadjusted")
# Normality check
qqnorm(residuals(pwsVel_home_multi_vid_unadj), main = "Normal Q-Q plot: Residuals All Video Metrics Unadjusted")
# tidy results
pwsVel_home_multi_vid_unadj_df <- tidy(pwsVel_home_multi_vid_unadj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(pwsVel_home_multi_vid_unadj)$R2_conditional) %>%
mutate(R2_marginal = r2(pwsVel_home_multi_vid_unadj)$R2_marginal) %>%
mutate(Model = "Unadjusted - Multivariate Video Metrics")
pwsVel_home_multi_vid_unadj_df## # A tibble: 7 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inter~ 152. 28.6 5.31 42.2 3.88e-6 94.2
## 2 fixed <NA> stride~ -31.5 16.9 -1.86 46.0 6.92e-2 -65.6
## 3 fixed <NA> log_de~ 7.56 6.81 1.11 30.5 2.76e-1 -6.34
## 4 fixed <NA> mean_c~ 0.0295 0.120 0.246 26.5 8.08e-1 -0.217
## 5 fixed <NA> stride~ -0.363 0.871 -0.416 34.0 6.80e-1 -2.13
## 6 ran_pars bw_id sd__(I~ 24.4 NA NA NA NA NA
## 7 ran_pars Residual sd__Ob~ 7.70 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>Multivariate Video Models - Adjusted
pwsVel_home_multi_vid_adj <- lmer(PWS_velocitycmsecmean ~
stride_time_median_sec_pose_hv +
log_delta_pix_h_rel_median_pose_hv +
mean_cadence_step_per_min_pose_hv +
stride_width_median_cm_pose_hv +
demoEHR_Age +
demoEHR_DiseaseDuration +
ms_dx_condensed +
race_ethnicity_clean +
clean_sex +
(1|bw_id),
data = home_clean_df)
summary(pwsVel_home_multi_vid_adj)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PWS_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv +
## mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv +
## demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed +
## race_ethnicity_clean + clean_sex + (1 | bw_id)
## Data: home_clean_df
##
## REML criterion at convergence: 339
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.72579 -0.20156 0.00372 0.16820 1.96033
##
## Random effects:
## Groups Name Variance Std.Dev.
## bw_id (Intercept) 429.17 20.716
## Residual 54.33 7.371
## Number of obs: 51, groups: bw_id, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 104.167004 40.693556 32.401756
## stride_time_median_sec_pose_hv 0.492992 19.222242 37.800828
## log_delta_pix_h_rel_median_pose_hv 7.279419 6.603662 30.513459
## mean_cadence_step_per_min_pose_hv -0.009213 0.114462 28.674694
## stride_width_median_cm_pose_hv 0.157705 0.872278 31.865658
## demoEHR_Age 0.157894 0.493513 13.565744
## demoEHR_DiseaseDuration -0.140464 0.724737 14.202897
## ms_dx_condensedProgressive MS -48.717374 16.734043 23.234867
## ms_dx_condensedMS, Subtype Not Specified 2.408856 19.256910 14.386728
## race_ethnicity_cleanAsian 36.669986 24.223712 12.967084
## race_ethnicity_cleanHispanic or Latino -20.397090 25.171045 13.245653
## race_ethnicity_cleanOther/Unknown/Declined 13.467405 16.027845 13.040109
## clean_sexMale 25.362494 13.086548 14.385849
## t value Pr(>|t|)
## (Intercept) 2.560 0.01533 *
## stride_time_median_sec_pose_hv 0.026 0.97967
## log_delta_pix_h_rel_median_pose_hv 1.102 0.27894
## mean_cadence_step_per_min_pose_hv -0.080 0.93641
## stride_width_median_cm_pose_hv 0.181 0.85767
## demoEHR_Age 0.320 0.75389
## demoEHR_DiseaseDuration -0.194 0.84907
## ms_dx_condensedProgressive MS -2.911 0.00781 **
## ms_dx_condensedMS, Subtype Not Specified 0.125 0.90218
## race_ethnicity_cleanAsian 1.514 0.15406
## race_ethnicity_cleanHispanic or Latino -0.810 0.43206
## race_ethnicity_cleanOther/Unknown/Declined 0.840 0.41591
## clean_sexMale 1.938 0.07250 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Residuals vs. fitted values
hist(resid(pwsVel_home_multi_vid_adj), main = "Residuals All Video Metrics Adjusted")
# Normality check
qqnorm(residuals(pwsVel_home_multi_vid_adj), main = "Normal Q-Q plot: Residuals All Video Metrics Adjusted")
# tidy results
pwsVel_home_multi_vid_adj_df <- tidy(pwsVel_home_multi_vid_adj, conf.int = TRUE) %>%
mutate(R2_conditional = r2(pwsVel_home_multi_vid_adj)$R2_conditional) %>%
mutate(R2_marginal = r2(pwsVel_home_multi_vid_adj)$R2_marginal) %>%
mutate(Model = "Adjusted - Multivariate Video Metrics")
pwsVel_home_multi_vid_adj_df## # A tibble: 15 x 13
## effect group term estimate std.error statistic df p.value conf.low
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Inte~ 1.04e+2 40.7 2.56 32.4 0.0153 21.3
## 2 fixed <NA> strid~ 4.93e-1 19.2 0.0256 37.8 0.980 -38.4
## 3 fixed <NA> log_d~ 7.28e+0 6.60 1.10 30.5 0.279 -6.20
## 4 fixed <NA> mean_~ -9.21e-3 0.114 -0.0805 28.7 0.936 -0.243
## 5 fixed <NA> strid~ 1.58e-1 0.872 0.181 31.9 0.858 -1.62
## 6 fixed <NA> demoE~ 1.58e-1 0.494 0.320 13.6 0.754 -0.904
## 7 fixed <NA> demoE~ -1.40e-1 0.725 -0.194 14.2 0.849 -1.69
## 8 fixed <NA> ms_dx~ -4.87e+1 16.7 -2.91 23.2 0.00781 -83.3
## 9 fixed <NA> ms_dx~ 2.41e+0 19.3 0.125 14.4 0.902 -38.8
## 10 fixed <NA> race_~ 3.67e+1 24.2 1.51 13.0 0.154 -15.7
## 11 fixed <NA> race_~ -2.04e+1 25.2 -0.810 13.2 0.432 -74.7
## 12 fixed <NA> race_~ 1.35e+1 16.0 0.840 13.0 0.416 -21.1
## 13 fixed <NA> clean~ 2.54e+1 13.1 1.94 14.4 0.0725 -2.63
## 14 ran_pars bw_id sd__(~ 2.07e+1 NA NA NA NA NA
## 15 ran_pars Residual sd__O~ 7.37e+0 NA NA NA NA NA
## # i 4 more variables: conf.high <dbl>, R2_conditional <dbl>, R2_marginal <dbl>,
## # Model <chr>ANOVA - Compare Multivar Models
p value < 0.05, more complex model is significantly better than the simpler model
https://bookdown.org/ndphillips/YaRrr/comparing-regression-models-with-anova.html
# Demographics only vs Video + Demographics
anova(pwsVel_home_multi_dem, pwsVel_home_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: home_clean_df
## Models:
## pwsVel_home_multi_dem: PWS_velocitycmsecmean ~ demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## pwsVel_home_multi_vid_adj: PWS_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## pwsVel_home_multi_dem 11 422.14 443.39 -200.07 400.14
## pwsVel_home_multi_vid_adj 15 427.92 456.90 -198.96 397.92 2.2177 4
## Pr(>Chisq)
## pwsVel_home_multi_dem
## pwsVel_home_multi_vid_adj 0.6958# Video only vs Video + Demographics
anova(pwsVel_home_multi_vid_unadj, pwsVel_home_multi_vid_adj)## refitting model(s) with ML (instead of REML)## Data: home_clean_df
## Models:
## pwsVel_home_multi_vid_unadj: PWS_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + (1 | bw_id)
## pwsVel_home_multi_vid_adj: PWS_velocitycmsecmean ~ stride_time_median_sec_pose_hv + log_delta_pix_h_rel_median_pose_hv + mean_cadence_step_per_min_pose_hv + stride_width_median_cm_pose_hv + demoEHR_Age + demoEHR_DiseaseDuration + ms_dx_condensed + race_ethnicity_clean + clean_sex + (1 | bw_id)
## npar AIC BIC logLik deviance Chisq Df
## pwsVel_home_multi_vid_unadj 7 432.55 446.07 -209.27 418.55
## pwsVel_home_multi_vid_adj 15 427.92 456.90 -198.96 397.92 20.623 8
## Pr(>Chisq)
## pwsVel_home_multi_vid_unadj
## pwsVel_home_multi_vid_adj 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save all Multivar R-squared values
pwsVel_home_multivar_all <- bind_rows(pwsVel_home_multi_dem_df,
pwsVel_home_multi_vid_unadj_df,
pwsVel_home_multi_vid_adj_df)
# save R-squared
adj_r_squared_df <- pwsVel_home_multivar_all[c('Model', 'R2_conditional', 'R2_marginal')] %>%
distinct() %>% # remove duplicates
mutate(R2_conditional = round(R2_conditional, 2)) %>%
mutate(R2_marginal = round(R2_marginal, 2))
print(adj_r_squared_df)## # A tibble: 3 x 3
## Model R2_conditional R2_marginal
## <chr> <dbl> <dbl>
## 1 Unadjusted - Multivariate Demographics and MS Info 0.95 0.52
## 2 Unadjusted - Multivariate Video Metrics 0.92 0.15
## 3 Adjusted - Multivariate Video Metrics 0.95 0.53write.csv(adj_r_squared_df, file.path(output_dir, 'ZenoVelPWS_adj_vs_unadj_home_r2.csv'))Plot Unadjusted vs Adjusted Estimates
p <- adj_vs_unadj_plot(results_df = pwsVel_home_multivar_all,
plot_title = "Home: PWS Mat Velocity Unadjusted vs Adjusted",
x_adj = 30)
p
ggsave(file.path(output_dir, 'ZenoPWS_adj_vs_unadj_home_estimates.png'),
bg = "white", width= 12, height=10)